Text Summarization 2017


Notes:

  • Fuzzy relational clustering
  • Human aided machine summarization (HAMS)
  • Intweetive
  • Machine aided human summarization (MAHS)
  • Text summarizer

Resources:

Wikipedia:

References:

See also:

100 Best Automatic Summarization Videos100 Best GitHub: Automatic Summarization


Recent automatic text summarization techniques: a survey
M Gambhir, V Gupta – Artificial Intelligence Review, 2017 – Springer
Abstract As information is available in abundance for every topic on internet, condensing the important information in the form of summary would benefit a number of users. Hence, there is growing interest among the research community for developing new approaches to

Opinion mining from online hotel reviews–A text summarization approach
YH Hu, YL Chen, HL Chou – Information Processing & Management, 2017 – Elsevier
Abstract Online travel forums and social networks have become the most popular platform for sharing travel information, with enormous numbers of reviews posted daily. Automatically generated hotel summaries could aid travelers in selecting hotels. This study proposes a

Text summarization techniques: A brief survey
M Allahyari, S Pouriyeh, M Assefi, S Safaei… – arXiv preprint arXiv …, 2017 – arxiv.org
Abstract: In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to

Text summarization using unsupervised deep learning
M Yousefi-Azar, L Hamey – Expert Systems with Applications, 2017 – Elsevier
Abstract We present methods of extractive query-oriented single-document summarization using a deep auto-encoder (AE) to compute a feature space from the term-frequency (tf) input. Our experiments explore both local and global vocabularies. We investigate the effect

Word-sentence co-ranking for automatic extractive text summarization
C Fang, D Mu, Z Deng, Z Wu – Expert Systems with Applications, 2017 – Elsevier
Abstract Extractive summarization aims to automatically produce a short summary of a document by concatenating several sentences taken exactly from the original material. Due to its simplicity and easy-to-use, the extractive summarization methods have become the

Improving semantic relevance for sequence-to-sequence learning of chinese social media text summarization
S Ma, X Sun, J Xu, H Wang, W Li, Q Su – arXiv preprint arXiv:1706.02459, 2017 – arxiv.org
Abstract: Current Chinese social media text summarization models are based on an encoder-decoder framework. Although its generated summaries are similar to source texts literally, they have low semantic relevance. In this work, our goal is to improve semantic relevance

A model for text summarization
RM Alguliyev, RM Aliguliyev, NR Isazade… – International Journal of …, 2017 – igi-global.com
Abstract Text summarization is a process for creating a concise version of document (s) preserving its main content. In this paper, to cover all topics and reduce redundancy in summaries, a two-stage sentences selection method for text summarization is proposed. At

Automatic Keyword Extraction for Text Summarization: A Survey
SK Bharti, KS Babu – arXiv preprint arXiv:1704.03242, 2017 – arxiv.org
Abstract: In recent times, data is growing rapidly in every domain such as news, social media, banking, education, etc. Due to the excessiveness of data, there is a need of automatic summarizer which will be capable to summarize the data especially textual data in

Automatic Text Summarization for Indonesian Language Using TextTeaser
D Gunawan, A Pasaribu, RF Rahmat… – IOP Conference Series …, 2017 – iopscience.iop.org
Abstract Text summarization is one of the solution for information overload. Reducing text without losing the meaning not only can save time to read, but also maintain the reader’s understanding. One of many algorithms to summarize text is TextTeaser. Originally, this

Text summarization based on classification using ANFIS
YJ Kumar, FJ Kang, OS Goh, A Khan – Asian Conference on Intelligent …, 2017 – Springer
Abstract The information overload faced by today’s society has created a big challenge for people who want to look for relevant information from the internet. There are a lot of online documents available and digesting such large texts collection is not an easy task. Hence,

Extractive Based Automatic Text Summarization.
SM Patel, VK Dabhi, HB Prajapati – JCP, 2017 – jcomputers.us
Abstract: Automatic text summarization is the process of reducing the text content and retaining the important points of the document. Generally, there are two approaches for automatic text summarization: Extractive and Abstractive. The process of extractive based

A semantic relevance based neural network for text summarization and text simplification
S Ma, X Sun – arXiv preprint arXiv:1710.02318, 2017 – arxiv.org
Abstract: Text summarization and text simplification are two major ways to simplify the text for poor readers, including children, non-native speakers, and the functionally illiterate. Text summarization is to produce a brief summary of the main ideas of the text, while text

ATSSC: Development of an approach based on soft computing for text summarization
MA Tayal, MM Raghuwanshi, LG Malik – Computer Speech & Language, 2017 – Elsevier
Abstract Natural Language Processing (NLP) is a field of computer science and linguistics concerned with the unique conversation between computers and human languages. It processes data through Lexical analysis, Syntax analysis, Semantic analysis, Discourse

Semantic Graph Based Automatic Text Summarization for Hindi Documents Using Particle Swarm Optimization
V Dalal, L Malik – … on Information and Communication Technology for …, 2017 – Springer
Abstract Automatic text summarization can be defined as a process of extracting and describing important information from given document using computer algorithms. A number of techniques have been proposed by researchers in the past for summarization of English

Evaluation of Unsupervised Learning based Extractive Text Summarization Technique for Large Scale Review and Feedback Data
JP Verma, A Patel – Indian Journal of Science and Technology, 2017 – indjst.org
Background/Objectives: Supervised techniques uses human generated summary to select features and parameter for summarization. The main problem in this approach is reliability of summary based on human generated parameters and features. Many researches have

A novel technique for multidocument Hindi text summarization
AN Gulati, SD Sawarkar – Nascent Technologies in Engineering …, 2017 – ieeexplore.ieee.org
A text summary is a reduction of original text to summarized text by selecting what is important in the source. Over a period of years the World Wide Web has expanded so that tremendous amount of data is created and available online. Text summarization is needed

A survey on extractive text summarization
N Moratanch, S Chitrakala – Computer, Communication and …, 2017 – ieeexplore.ieee.org
Text Summarization is the process of obtaining salient information from an authentic text document. In this technique, the extracted information is achieved as a summarized report and conferred as a concise summary to the user. It is very crucial for humans to understand

MSATS: Multilingual sentiment analysis via text summarization
R Bhargava, Y Sharma – Cloud Computing, Data Science & …, 2017 – ieeexplore.ieee.org
Sentiment Analysis has been a keen research area for past few years. Though much of the exploration that has been done supports English language only. This paper proposes a method using which one can analyze different languages to find sentiments in them and

A similarity-based abstract argumentation approach to extractive text summarization
S Ferilli, A Pazienza, S Angelastro, A Suglia – Conference of the Italian …, 2017 – Springer
Abstract Sentence-based extractive summarization aims at automatically generating shorter versions of texts by extracting from them the minimal set of sentences that are necessary and sufficient to cover their content. Providing effective solutions to this task would allow the

An improved method of automatic text summarization for web contents using lexical chain with semantic-related terms
HM Lynn, C Choi, P Kim – Soft Computing, 2017 – Springer
Abstract Many researches have been converging on automatic text summarization as increasing of text documents due to the expansion of information diffusion constantly. The objective of this proposal is to achieve the most reliable and substantial context or most

Centroid-based text summarization through compositionality of word embeddings
G Rossiello, P Basile, G Semeraro – … of the MultiLing 2017 Workshop on …, 2017 – aclweb.org
Abstract The textual similarity is a crucial aspect for many extractive text summarization methods. A bag-of-words representation does not allow to grasp the semantic relationships between concepts when comparing strongly related sentences with no words in common. To

Semantic text summarization of long videos
S Sah, S Kulhare, A Gray… – … of Computer Vision …, 2017 – ieeexplore.ieee.org
Long videos captured by consumers are typically tied to some of the most important moments of their lives, yet ironically are often the least frequently watched. The time required to initially retrieve and watch sections can be daunting. In this work we propose novel

Deep Recurrent Generative Decoder for Abstractive Text Summarization
P Li, W Lam, L Bing, Z Wang – arXiv preprint arXiv:1708.00625, 2017 – arxiv.org
We propose a new framework for ab- stractive text summarization based on a sequence-to-sequence oriented encoder- decoder model equipped with a deep re- current generative decoder (DRGN). La- tent structure information implied in the target summaries is learned based on a

An additive FAHP based sentence score function for text summarization
A Güran, M Uysal, Y Ekinci… – Information Technology …, 2017 – pdfs.semanticscholar.org
This study proposes a novel additive Fuzzy Analytical Hierarchy Process (FAHP) based sentence score function for Automatic Text Summarization (ATS), which is a method to handle growing amounts of textual data. ATS aims to reduce the size of a text while covering

Text Summarization using Abstract Meaning Representation
S Dohare, H Karnick – arXiv preprint arXiv:1706.01678, 2017 – arxiv.org
Abstract: Summarization of large texts is still an open problem in language processing. In this work we develop a full fledged pipeline to generate summaries of news articles using the Abstract Meaning Representation (AMR). We first generate the AMR graphs of stories

Text summarization from legal documents: a survey
A Kanapala, S Pal, R Pamula – Artificial Intelligence Review, 2017 – Springer
Abstract Enormous amount of online information, available in legal domain, has made legal text processing an important area of research. In this paper, we attempt to survey different text summarization techniques that have taken place in the recent past. We put special

Automatic Text Summarization of Video Lectures Using Subtitles
S Garg – … in Intelligent Computing, Communication and Devices, 2017 – Springer
Abstract Text summarization can be defined as a process of reducing a text document using computer program in order to generate a summary of original document that consists of most important things covered in that. An example of summarization technology is search engines

Improving Social Media Text Summarization by Learning Sentence Weight Distribution
X Jing-Jing, S Xu, R Xuan-Cheng – 2017 – paper.edu.cn
Recently, encoder-decoder models are widely used in social media text summarization. However, these models sometimes select noise words in irrelevant sentences as part of a summary by error, thus declining the performance. In order to inhibit irrelevant sentences

Text Summarization using Sentence Scoring Method
TSR Raju, B Allarpu – 2017 – irjet.net
Abstract-In this project an automated text summarization tool has been developed using Sentence Scoring Method which involves finding the frequent terms, sentence ranking etc. Summary is extracted from the list of top ranked sentences. The size of summary can be

Short Text Summarization using Topic Modeling Algorithm
N Sahu, V Chawra – ijetmas.com
Abstract—In modern days, the day-to-day hustle-bustle does not allow a human being to assign time for manually summarizing variety of lengthy documents. Hence it is of ultimate importance to devise for an application that will make possible the automated text

USI Participation at SMERP 2017 Text Summarization Task
A Giachanou, I Mele, F Crestani – analysis, 2017 – ceur-ws.org
Abstract. This short report describes the participation of the Universita della Svizzera italiana (USI) at the SMERP Workshop Data Challenge Track for the task text summarization of Level 1. Our participation is based on a linear interpolation for combining relevance and novelty

IMPROVE THE QUALITY OF IMPORTANT SENTENCES FOR AUTOMATIC TEXT SUMMARIZATION
M George – Computer Science & Information Technology – pdfs.semanticscholar.org
ABSTRACT There are sixteen known methods for automatic text summarization. In our study we will use Natural language processing NLP within hybrid approach that will improve the quality of important sentences selection by thickening sentence score along with reducing

MULTILINGUAL TEXT SUMMARIZATION TECHNIQUES
AS Sherry – pdfs.semanticscholar.org
Abstract A Summary is a short document that represents the essential information from the given document. Text Summarization represents essential text information by leaving the irrelevant detail, reduces the details and contents them in short way that meet with

CSTS: Cuckoo Search Based Model for Text Summarization
R Rautray, RC Balabantaray – Artificial Intelligence and Evolutionary …, 2017 – Springer
Abstract Exponential growth of information in the web became infeasible for user to sieve useful information very quickly. So solution to such problem now a day is text summarization. Text summarization is the process of creating condensed version of original text by

Abstractive Text Summarization
S Singhal, A Bhattacharya – iitk.ac.in
Abstract Neural Sequence to Sequence attention models have shown promising results in Abstractive Text Summarization. But they are plagued by various problems. The summaries are often repetitive and absurd. We explore and review different techniques that can help

An Innovative Approach of Bangla Text Summarization by Introducing Pronoun Replacement and Improved Sentence Ranking.
M Haque, S Pervin, Z Begum – Journal of Information …, 2017 – search.ebscohost.com
Abstract This paper proposes an automatic method to summarize Bangla news document. In the proposed approach, pronoun replacement is accomplished for the first time to minimize the dangling pronoun from summary. After replacing pronoun, sentences are ranked using

Using Statistical and Semantic Analysis for Arabic Text Summarization
N Alami, Y El Adlouni, N En-nahnahi… – … Conference on Information …, 2017 – Springer
Abstract Automatic text summarization is an essential tool to overcome the problem of information overload. So far this field has not been studied enough for Arabic language and currently only few related works are available. Arabic text summarization is faced with two

Addressing the Problem of Coherence in Automatic Text Summarization: A Latent Semantic Analysis Approach
A Omar – International Journal of English Linguistics, 2017 – ccsenet.org
Abstract This article is concerned with addressing the problem of coherence in the automatic summarization of prose fiction texts. Despite the increasing advances within the summarization theory, applications and industry, many problems are still unresolved in

A Method for Semantic Relatedness Based Query Focused Text Summarization
N Rahman, B Borah – … Conference on Pattern Recognition and Machine …, 2017 – Springer
Abstract In this paper, a semantic relatedness based query focused text summarization technique is introduced to find relevant information from single text document. This semantic relatedness measure extracts the related sentences according to the query. The query

Improving text summarization using neuro-fuzzy approach
M Azhari, Y Jaya Kumar – Journal of Information and …, 2017 – Taylor & Francis
ABSTRACT In today’s digital era, it becomes a challenge for netizens to find specific information on the internet. Many web-based documents are retrieved and it is not easy to digest all the retrieved information. Automatic text summarization is a process that identifies

Text Summarization Using Abstractive Methods
P Rawat, NG Ganpatrao, D Gupta – Journal of Network Communications …, 2017 – jncet.org
Abstract–Text summarization is the procedure of extracting vital and important information from the given source text and to produce that information to the user in the form of a summary. Sometimes it becomes very difficult for humans to manually summarize a large

Deep Learning in the Domain of Multi-Document Text Summarization
RK Roul, JK Sahoo, R Goel – International Conference on Pattern …, 2017 – Springer
Abstract Text summarization is the process of generating a shorter version of the input text which captures its most important information. This paper addresses and tries to solve the problem of extractive text summarization which works by selecting a subset of phrases or

Automatically identifying facet roles from comparative structures to support biomedical text summarization
A Lucic – 2017 – ideals.illinois.edu
Within the context of biomedical scholarly articles, comparison sentences represent a rhetorical structure commonly used to communicate findings. More generally, comparison sentences are rich with information about how the properties of one or more entities relate

A Semantic QA-Based Approach for Text Summarization Evaluation
P Chen, F Wu, T Wang – arXiv preprint arXiv:1704.06259, 2017 – arxiv.org
Abstract: Many Natural Language Processing and Computational Linguistics applications involves the generation of new texts based on some existing texts, such as summarization, text simplification and machine translation. However, there has been a serious problem

Extraction and Classification of Linguistic Text for Text Summarization
N Roy, R Chatterjee, S Sanyal, C Misra – 2017 – researchgate.net
Abstract Text summarization is one of the most exigent fields in Natural Language Processing. Classification of text and keyword fetching from a sentence is the most intrinsic part of text summarization. The method propounded in this paper is immensely coherent for

Automatic Arabic Text Summarization Approaches
S Lagrini, M Redjimi, N Azizi – International Journal of …, 2017 – search.proquest.com
Abstract In recent years, automatic text summarization has seen renewed interest, and has been experiencing an increasing number of researches and products especially in English language. However, in Arabic language, little works and limited researches have been done

Computational models for text summarization
L Keselman, L Schubert – leonidk.com
Abstract Abstractive text summarization is a blossoming area of natural language processing research in which short textual summaries are generated from longer input documents. Existing state-of-the-art methods take long time to train, and are limited to functioning on

MULTI DOCUMENT TEXT SUMMARIZATION USING BACKPROPAGATION NETWORK
A Giradkar, SD Sawarkar, A Gulati – 2017 – irjet.net
Abstract-For English language lots of research work has been carried out in the field of text summarization but not for Hindi language. In the proposed system idea is to summarize multiple Hindi documents. This summarization is based on features extracted from

Text Summarization: A Review
TC Chitranjan, V Doshi… – Imperial Journal of …, 2017 – imperialjournals.com
Abstract: Text summarization is a process of extracting or collecting important information from original text and presents that information in the form of summary. Text summarization has become the necessity of many applications for example search engine, business

Automatic Amharic Text Summarization using NLP Parser
GT Mekuria, AS Jagtap – ijettjournal.org
Abstract-The proposed system investigates the problem of building the domain based single and multiple document Amharic text summarization. Multi-document summarization is the main task in natural language processing and summarizing a huge text document into a

A Comprehensive Survey on Extractive Text Summarization Techniques
AS Asa, S Akter, MP Uddin, MD Hossain, SK Roy… – ajer.org
ABSTRACT: Automated data collection tools and matured database technology lead to tremendous amounts of data stored in database, data warehouses and other data repositories. With the increasing amount of online information, it becomes extremely difficult

Automatic Debate Text Summarization in Online Debate Forum
AD Chowanda, AR Sanyoto, D Suhartono… – Procedia Computer …, 2017 – Elsevier
Abstract The goal of this research is to create a system that can generate summaries from online debate forum by using abstractive technique. This research is based on the point-based summarization technique, where a point is a verb and its syntactic arguments. The

Multi-document Text Summarization Using Sentence Extraction
R Ahuja, W Anand – Artificial Intelligence and Evolutionary Computations …, 2017 – Springer
Abstract This paper presents a method for generating multi-document text summary building on single document text summaries and by combining those single document text summaries using cosine similarity. For the generation of single document text summaries

Integrated approach to the development of text summarization system
?? ??????? – 2017 – rep.bntu.by
In this information and communication technology era designing interactive computer systems that are effective, efficient, easy and enjoyable to use is becoming increasingly important. Of the numerous ways explored by researchers to enhance Human-Computer

Arabic Single-Document Text Summarization Using Particle Swarm Optimization Algorithm
RZ Al-Abdallah, AT Al-Taani – Procedia Computer Science, 2017 – Elsevier
Abstract In this research, we propose the use of Particle Swarm Optimization (PSO) algorithm for the extraction of summaries for single Arabic documents. The PSO approach is compared with evolutionary approaches that use Genetic Algorithms (GA) and Harmony

Search engines over text summarization
M Jia – 2017 – search.proquest.com
Abstract We study how to construct a search engine to return text information to the user accurately, concisely, and efficiently without the need of reading through a long list of returned documents. For this purpose, we embed text summarization algorithms into a

A Heuristic Approach of Text Summarization for Bengali Documentation
S Abujar, M Hasan, MSI Shahin… – … and Networking (8th …, 2017 – researchgate.net
Abstract—Automated Text Summarization is a technique of summarizing any document or text automatically. Summarized text is the concise form of the given text. In Natural language processing many text summarization techniques are available for English language, but only

How far we can go with extractive text summarization? Heuristic methods to obtain near upper bounds
WM Wang, Z Li, JW Wang, ZH Zheng – Expert Systems with Applications, 2017 – Elsevier
Abstract Extractive text summarization is an effective way to automatically reduce a text to a summary by selecting a subset of the text. The performance of a summarization system is usually evaluated by comparing with human-constructed extractive summaries that are

Automatic Text Summarization
R Chettri, UK Chakraborty – International Journal of …, 2017 – pdfs.semanticscholar.org
ABSTRACT Summarization is the art of abstracting key content from one or more information sources [6]. Summarization includes text summarization, image summarization, and video summarization. Text summarization is one of application of natural language processing and

A Survey of Extractive Arabic Text Summarization Approaches
S Lagrini, M Redjimi, N Azizi – International Conference on Arabic …, 2017 – Springer
Abstract Automatic text summarization is an important research area originating from the late 50’s but not losing its celebrity until now. Over the past half a century, automatic text summarization has seen a great interest especially in English language. However, in Arabic

Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approach
JM Sanchez-Gomez, MA Vega-Rodríguez… – Knowledge-Based …, 2017 – Elsevier
Abstract Automatic text summarization methods are increasingly needed nowadays. Extractive multi-document summarization approaches aim to obtain the main content of a document collection at the same time that the redundant information is reduced. This can be

APPROACH FOR THICKENING SENTENCE SCORE FOR AUTOMATIC TEXT SUMMARIZATION
M George – aircconline.com
ABSTRACT In our study we will use approach that combine Natural language processing NLP with Term occurrences to improve the quality of important sentences selection by thickening sentence score along with reducing the number of long sentences that would be

Saaraansh: Gujarati Text Summarization System
J Sheth, B Patel – ijcsits.org
Abstract-The domain of natural language processing has moved from international language processing like English to national language processing like Hindi. In the world of information overload, end users are benefited with summaries of given text document.

POS-Tagging Enhanced Korean Text Summarization
W Liu, L Wang – International Conference on Intelligent Computing, 2017 – Springer
Abstract Information explosion causes a serious scarcity of people’s time and a severe divergence of people’s attention. This paper addresses the issue of automatic summarization for Korean texts and presents a novel keyword-extraction-based Korean text

CS585 Project Report Long Text Summarization using Neural Networks and Rule-Based Approach
S Liu – 2017 – shujianliu.com
1 Abstract Automatic text summarization is the task for computers to produce a concise and fluent summary conveying the key information in the input. There are generally two types of automatic texts summarization: extraction and abstraction. Many papers has been published

Improving Triangle-Graph Based Text Summarization using Hybrid Similarity Function
YA AL-Khassawneh, N Salim, M Jarrah – Indian Journal of Science and …, 2017 – indjst.org
Objective: Extractive Summarization, extracts the most applicable sentences from the main document, while keeping the most vital information in the document. The Graph-based techniques have become very popular for text summarisation. This paper introduces a

Automatic Text Summarization Approaches to Speed up Topic Model Learning Process
M Morchid, JM Torres-Moreno, R Dufour… – arXiv preprint arXiv …, 2017 – arxiv.org
Abstract: The number of documents available into Internet moves each day up. For this reason, processing this amount of information effectively and expressibly becomes a major concern for companies and scientists. Methods that represent a textual document by a topic

Clustering Based Text Summarization on Comments from Hotel Services Using IncreSTS Algorithm
B Dhanalakshmi… – Journal of Computational …, 2017 – ingentaconnect.com
In the past few years, the impact of Social Networking Services (SNS) has risen the need for the clients of the social networking to obtain a short comprehension of a general stream without the whole comment list being read. In the present manuscript, the study centers on

A Novel Architecture for Agent Based Text Summarization
A Asthana, EV Tiwari, EMC Pandey… – Asian Journal of Applied …, 2017 – ajast.net
ABSTRACT Search engines deal huge volume of documents, even they output a large number of documents for a given user’s query. Under these Circumstances it became very difficult for the user to find the document he actually needs, because most of the users are

Comparison of Document Index Graph Using TextRank and HITS Weighting Method in Automatic Text Summarization
F Hadyan, MA Bijaksana – Journal of Physics: Conference …, 2017 – iopscience.iop.org
Abstract Automatic summarization is a system that can help someone to take the core information of a long text instantly. The system can help by summarizing text automatically. there’s Already many summarization systems that have been developed at this time but

Comprehensive and Evolution Study Focusing on Comparative Analysis of Automatic Text Summarization
R Patel, A Thakkar, K Makwana, J Patel – International Conference on …, 2017 – Springer
Abstract In the escalating trend of atomization and online information, text summarization bolster in perceiving textual information in the form of summary. It’s highly tedious for human beings to manually summarize large documents of text. In this paper, a study on abstractive

Performance analysis of keyword extraction algorithms assessing extractive text summarization
A Kumar, A Sharma, S Sharma… – Computer …, 2017 – ieeexplore.ieee.org
Automatic text summarization is the task of deriving a meaningful and concise brief from a given text while retaining the concept and key information conveyed by the original text. So far, numerous approaches and algorithms have been devised to achieve this goal with

Less-redundant Text Summarization using Ensemble Clustering Algorithm based on GA and PSO
JS LEE, H HAHM, SC PARK – pdfs.semanticscholar.org
Abstract: In this paper, a novel text clustering technique is proposed to summarize text documents. The clustering method, so called ‘Ensemble Clustering Method’, combines both genetic algorithms (GA) and particle swarm optimization (PSO) efficiently and automatically

Generative Adversarial Network for Abstractive Text Summarization
L Liu, Y Lu, M Yang, Q Qu, J Zhu, H Li – arXiv preprint arXiv:1711.09357, 2017 – arxiv.org
Abstract: In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which

The ROUGE-AR: A Proposed Extension to the ROUGE Evaluation Metric for Abstractive Text Summarization
S Maples – stanford.edu
Abstract Abstractive text summarization refers to summary generation that is based on semantic understanding, and is thus not strictly limited to the words found in the source. Despite its success in deep learning, however, the task of text summarization has no reliably

Bio-Inspired Algorithms for Text Summarization: A Review
R Rautray, RC Balabantaray – Bio-Inspired Computing for …, 2017 – books.google.com
ABSTRACT In last few decades, Bio-inspired algorithms (BIAs) have gained a significant popularity to handle hard real world and complex optimization problem. The scope and growth of Bio Inspired algorithms explore new application areas and computing

Text Summarization for Aspect-Polarity Extraction
SS Yande – INTERNATIONAL JOURNAL, 2017 – ijasret.com
Abstract—Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker. The applications of sentiment analysis are broad and powerful. The

Abstractive Text Summarization using Attentive Sequence-to-Sequence RNNs
E Jobson, A Gutiérrez – stanford.edu
Abstract In this work, we aimed to emulate the baseline of state-of-the-art abstractive text summarization models, with the intention of exploring different attention mechanisms upon having a decent working baseline. We decided to get inspiration specifically from the

Biogeography-Based Optimization Algorithm for Automatic Extractive Text Summarization
H MirShojaee, B Masoumi, E Zeinali – International Journal of …, 2017 – ijiepr.iust.ac.ir
Given the increasing number of documents, sites, online sources, and the users’ desire to quickly access information, automatic textual summarization has caught the attention of many researchers in this field. Researchers have presented different methods for text

Multi-Document Text Summarization for Competitor Intelligence: A Framework
S Chakraborti – 2017 – iimidr.ac.in
Abstract Proliferation of web as an easily accessible information resource has led many business organizations to gather competitor intelligence (CI) directly from various resources on the internet, namely, news, blogs, reports, reviews etc. While collection of such

Using Topic Labels for Text Summarization
W Kou, F Li, Z Ye – … Conference on Industrial, Engineering and Other …, 2017 – Springer
Abstract Multi-document summarization is a difficult natural language processing task. Many extractive summarization methods consist of two steps: extract important concepts of documents and select sentences based on those concepts. In this paper, we introduce a

K nearest neighbor for text summarization using feature similarity
T Jo – Communication, Control, Computing and Electronics …, 2017 – ieeexplore.ieee.org
In this research, we propose a particular version of KNN (K Nearest Neighbor) where the similarity between feature vectors is computed considering the similarity among attributes or features as well as one among values. The task of text summarization is viewed into the

Automatic Keyword Extraction for Text Summarization in Multi-document e-Newspapers Articles
SK Bharti, KS Babu, A Pradhan, SPA Devi… – European Journal of …, 2017 – ejaet.com
ABSTRACT Summarization is the way towards lessening the content of a text file to make it brief that holds all the critical purposes in the content of original text file. In the process of extractive summarization, one extracts only those sentences which are the most relevant

Intweetive Text Summarization
E SANJUAN, M EL-B – gelbukh.com
ABSTRACT The amount of user generated contents from various social medias allows analyst to handle a wide view of conversations on several topics related to their business. Nevertheless keeping upto-date with this amount of information is not humanly feasible.

LaSTUS/TALN@ CLSciSumm-17: cross-document sentence matching and scientific text summarization systems
A Abura’ed, L Chiruzzo, H Saggion, P Accuosto… – 2017 – repositori.upf.edu
In recent years there has been an increasing interest in approaches to scienti c summarization that take advantage of the citations a research paper has received in order to extract its main contributions. In this context, the CL-SciSumm 2017 Shared Task has been

Semantic Analysis Based Text Summarization
H Desai, H Moiyadi, D Pawar, G Agrawal, N Patil – pdfs.semanticscholar.org
Abstract—Automatic summarization has become an important part in the study of natural language processing since the advent of the 21st century, since a majority of the data online is textual. Summarization of text will lead to a reduction of data while maintaining the context

On (Commercial) Benefits of Automatic Text Summarization Systems in the News Domain: A Case of Media Monitoring and Media Response Analysis
P Modaresi, P Gross, S Sefidrodi, M Eckhof… – arXiv preprint arXiv …, 2017 – arxiv.org
Abstract: In this work, we present the results of a systematic study to investigate the (commercial) benefits of automatic text summarization systems in a real world scenario. More specifically, we define a use case in the context of media monitoring and media

Two-level text summarization from online news sources with sentiment analysis
TB Mirani, S Sasi – Networks & Advances in Computational …, 2017 – ieeexplore.ieee.org
People tend to read multiple news articles on a topic since a single article may not contain all important information. A summary of all the articles related to topic will save the time and energy. Text Summarization is a way of minimizing a textual document to a meaningful

Automated text summarisation and evidence-based medicine: A survey of two domains
A Sarker, D Molla, C Paris – arXiv preprint arXiv:1706.08162, 2017 – arxiv.org
Abstract: The practice of evidence-based medicine (EBM) urges medical practitioners to utilise the latest research evidence when making clinical decisions. Because of the massive and growing volume of published research on various medical topics, practitioners often find

TOPSIS with Multiple Linear Regression for Multi-Document Text Summarization
S Malallah, ZH Ali – ijs.scbaghdad.edu.iq
Abstract The huge amount of information in the internet makes rapid need of text summarization. Text summarization is the process of selecting important sentences from documents with keeping the main idea of the original documents. This paper proposes a

Psychological Features for Automatic Text Summarization
DE Losada, J Parapar – International Journal of Uncertainty …, 2017 – World Scientific
Automatically summarizing a document requires conveying the important points of a large document in only a few sentences. Extractive strategies for summarization are based on selecting the most important sentences from the input document (s). We claim here that

A Combination Method for Improving Text Summarization
Z Mashreghi, M Esmaeili – International Journal of Computer …, 2017 – search.proquest.com
Abstract As the volume of online and electronic information increasingly has grown, quickly and accurately access to these important resources is a big challenge. Text analytics can help by transposing words and sentences in unstructured data into high-quality information.

Context Sensitive Query Correction Method for Query-Based Text Summarization
N Rahman, B Borah – … Conference on Computational Science and Its …, 2017 – Springer
Abstract Contextual spell correction is very important for real word error correction. It gives the correct word for an incorrect word in a particular sentence. The traditional spell checker can correct those misspelled words which are not present in dictionary but here we try to

A Survey of Text Summarization Techniques for Indian Regional Languages
S Shimpikar, S Govilkar – International Journal of …, 2017 – pdfs.semanticscholar.org
ABSTRACT Summarization is the process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. Technologies that can make a coherent summary take into account variables

Tackling Biomedical Text Summarization: OAQA at BioASQ 5B
K Chandu, A Naik, A Chandrasekar, Z Yang, N Gupta… – BioNLP 2017, 2017 – aclweb.org
Abstract In this paper, we describe our participation in phase B of task 5b of the fifth edition of the annual BioASQ challenge, which includes answering factoid, list, yes-no and summary questions from biomedical data. We describe our techniques with an emphasis on

Holographic Lexical Chain and Its Application in Chinese Text Summarization
S Hou, Y Huang, C Fei, S Zhang, R Lu – … Joint Conference on Web and Big …, 2017 – Springer
Abstract Lexical chain has been widely used in many NLP areas. However, when using it for Web text summarization, especially for domain-specific text summarization, we got low accuracy results. The main reason is that traditional lexical chains only take nouns into

Abstractive Text Summarization with Quasi-Recurrent Neural Networks
P Adelson, S Arora, J Hara – pdfs.semanticscholar.org
Abstract We investigate a recent neural net model-Quasi-Recurrent Neural Networksand their application to abstractive text summarization, specifically generating a headline from the text of a news article. We use an encoder-decoder with attention model. We compare a

Autonomous Text Summarization Using Collective Intelligence Based on Nature-Inspired Algorithm
KG Tefrie, KA Sohn – International Conference on Mobile and Wireless …, 2017 – Springer
Abstract Thousands of years ago written language was introduced as a way of enhancing and facilitating communication. Fast forward to the twenty first century much has changed, especially the flow of data incrementing at fast rate and we should use the power of

Methods of sentence extraction, abstraction and ordering for automatic text summarization
MT Nayeem – 2017 – uleth.ca
In this thesis, we have developed several techniques for tackling both the extractive and abstractive text summarization tasks. We implement a rank based extractive sentence selection algorithm. For ensuring a pure sentence abstraction, we propose several novel

Automatic Text Summarization Using Reinforcement Learning with Embedding Features
GH Lee, KJ Lee – Proceedings of the Eighth International Joint …, 2017 – aclweb.org
Abstract An automatic text summarization system can automatically generate a short and brief summary that contains a main concept of an original document. In this work, we explore the advantages of simple embedding features in Reinforcement leaning approach to

Integrating Extractive and Abstractive Models for Long Text Summarization
S Wang, X Zhao, B Li, B Ge… – Big Data (BigData …, 2017 – ieeexplore.ieee.org
With the explosive growth of information on the Internet, it becomes more and more important to improve the efficiency of information acquisition. Automatic text summarization provides a good means for quick acquisition of information through compression and

Improving Social Media Text Summarization by Learning Sentence Weight Distribution
J Xu – arXiv preprint arXiv:1710.11332, 2017 – arxiv.org
Abstract: Recently, encoder-decoder models are widely used in social media text summarization. However, these models sometimes select noise words in irrelevant sentences as part of a summary by error, thus declining the performance. In order to inhibit

Extractive Text Summarization Implemented with SKG Formula
N Kavdavid, R Matoba – tc (w, d), 2017 – anlp.jp
The amount of information available today is tremendous and the problem of finding the relevant pieces and making sense of these is becoming more and more essential. Nowadays, a great deal of information comes from the Internet in a textual form. The

Partitioned-Based Clustering Approaches for Single Document Extractive Text Summarization
P Subba, S Ghosh, R Roy – International Conference on Mining …, 2017 – Springer
Abstract This article presents an extractive text summarization technique for single document using partition based clustering algorithms. Clustering of sentences is performed where the importance of each sentence in a document is attributed with three features namely, term

Automatic Arabic Text Summarization for Large Scale Multiple Documents Using Genetic Algorithm and MapReduce
RS Baraka, SN Al Breem – Information and Communication …, 2017 – ieeexplore.ieee.org
Multi document summarization focuses on extracting the most significant information from a collection of textual documents. Most summarization techniques require the data to be centralized, which may not be feasible in many cases due to computational and storage

Study of Abstractive Text Summarization Techniques
S Yeasmin, PB Tumpa, AM Nitu, M Palash – ajer.org
ABSTRACT: Nowadays, people use the internet to find information through information retrieval tools such as Google, Yahoo, Bing and so on. Because of the increasing rate of data, people need to get meaningful information. So, it is not possible for users to read each

Different approaches for identifying important concepts in probabilistic biomedical text summarization
M Moradi, N Ghadiri – Artificial intelligence in medicine, 2017 – Elsevier
Abstract Automatic text summarization tools help users in the biomedical domain to acquire their intended information from various textual resources more efficiently. Some of biomedical text summarization systems put the basis of their sentence selection approach

A Meticulous Approach for Extractive based Hindi Text Summarization using Genetic Algorithm
G Pareek, D Modi, A Athaiya – 2017 – academicscience.co.in
ABSTRACT Due to the rapid growth of technology and Internet, online information is overloaded on the web. Therefore it is very difficult to search useful information from the heap of data. Automatic text summarization (ATS) is an approach which, condenses the

Abstractive Text Summarization Using Deep Learning
CX Tran – 2017 – dspace.jaist.ac.jp
Abstract Text summarization is one of the most active research in natural language processing. Even though the history of text summarization dates back to 1950s, a majority of research focuses on extractive summarization in which we select some sentences from the

AUTOMATIC TEXT SUMMARIZATION
R Aggarwal, L Gupta – 2017 – pdfs.semanticscholar.org
Abstract-Content summarization is an old challenge however the modern research look into courses occupies towards rising patterns in biomedicine, item audit, instruction areas, messages and web journals. This is because of the way that there is data over-burden in

A survey on Real-Time Accumulative Short Text Summarization on Comment Streams
NV Kumar, MJ Reddy – ijsetr.org
Abstract: This paper concentrates on the issue of short content rundown on the remark stream of a particular message from informal community administrations (SNS). Since unmistakable clients will ask for the rundown at any minute, existing grouping strategies

Application of Minimum Vertex Cover for Keyword–based Text Summarization Process
AU Islam, B Kalita – International Journal of Computational …, 2017 – ripublication.com
International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 1 (2017), pp. 113-125 © Research India Publications http://www.ripublication.com … Application of Minimum Vertex Cover for Keyword –based Text Summarization Process … Atowar-Ul Islam1

Concept Based Text Document Summarization Using Domain Ontology
S Logeswari, R Gomathi, B Gomathy – 2017 – academicscience.co.in
… Text summarization or abstraction has always been a key activity in the information access context … [3] Aliguliyev, RM 2009 ‘A new sentence similarity measure and sentence based extractive technique for automatic text summarization’, Expert Systems with Applications, vol …

A survey on: Extractive text document summarization techniques
CS Yadav, R Kumar, PSS Aydav, HP Singh – advancedjournal.com
… Text summarization may be a feasible and powerful to handle such type of problems. Even text summarization can help to the government for “Good Governance” … The automation [8] of text summarization includes machine learning techniques, statics and phycology …

Multi-Document Summarization of Persian Text Using Paragraph Vectors
M Rohanian – Proceedings of the Student Research Workshop …, 2017 – acl-bg.org
… Their method is de- veloped based on the Extreme Learning Machine (ELM). There have been a few studies for Persian text summarization. Shakeri et al … In Text summarization branches out: Proceedings of the ACL-04 workshop, vol. 8. Hans Peter Luhn. 1958 …

Gist: general integrated summarization of text and reviews
J Lovinger, I Valova, C Clough – Soft Computing, 2017 – Springer
… Gist is a modular text summarization framework … Rather than attempting to create a catch-all system, we design Gist to allow changes within the existing system while focusing on core features that are essential for text summarization …

A New Method of Text Categorization and Summarization with Fuzzy Confusion Matrix
S Das, A Pal, G Sarker – ijettjournal.org
… Text Summarization achieves the task of compression of the text size while preserving the overall meaning. Text summarization can be classified as extrinsic or extractive summarization and intrinsic or abstractive summarization …

Summarization of Changes in Dynamic Text Collections using Latent Dirichlet Allocation Model
MN Nanaware, MD Daki, MS Dangeti, MS Nikam – 2017 – ijetsr.com
… collections. Along with standard text summarization, this retrieval techniques displays a summary to the user by capturing the major points expressed in the most recent version of an entire document in a compressed form. A …

Improving Multi-Document Summarization via Text Classification.
Z Cao, W Li, S Li, F Wei – AAAI, 2017 – aaai.org
… Genest, P.-E.; Gotti, F.; and Bengio, Y. 2011. Deep learn- ing for automatic summary scoring. In Proceedings of the Workshop on Automatic Text Summarization, 17–28. Gillick, D., and Favre, B. 2009. A scalable global model for summarization …

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