Abstractive Summarization – Meta-Guide.com

Notes:

Abstractive summarization (abstractive summarisation) is a method of generating a summary of a text by building an internal semantic representation of the content and then using natural language generation techniques to produce a summary that is closer to what a human might write. Abstractive summarization involves analyzing the meaning and intent of the original text, and then generating a summary that conveys the most important information in a concise and coherent manner.

Abstractive summarization can be used in a variety of contexts, such as to generate summaries of news articles, scientific papers, or other long-form texts. It can be particularly useful in situations where the original text is too long or complex to be easily understood by a general audience, or where a more concise and easily digestible summary is desired. Abstractive summarization can also be used to create summaries of spoken language, such as transcripts of interviews or lectures, by analyzing the meaning and intent of the spoken words and generating a summary that accurately conveys the main points of the original content.

Abstractive summarization requires the use of advanced natural language processing techniques, such as language modeling and machine translation, to analyze the meaning and structure of the original text and generate a summary that is coherent and natural-sounding. It can be a challenging task, as it requires a deep understanding of the content and context of the original text, as well as the ability to write in a clear and concise manner. However, when done well, abstractive summarization can produce highly accurate and effective summaries that are able to capture the essence of the original content in a concise and coherent form.

Extractive methods, on the other hand, select a subset of the most important sentences or phrases from the original text and concatenate them to form the summary. These summaries are typically composed of phrases and sentences that are directly taken from the original text, rather than being rephrased or rewritten in different words. Extractive methods are generally easier to implement and tend to produce more accurate results, but they can sometimes produce summaries that are choppy or difficult to understand because they do not take into account the overall structure and coherence of the original text.

Abstraction-based summarization refers to a type of summarization method that involves creating an internal semantic representation of the content of a text and then generating a summary that is based on this abstraction. This type of summarization involves analyzing the meaning and intent of the original text and creating a summary that conveys the most important information in a concise and coherent manner, often using different words and phrases than those used in the original text.
Abstractive text summarization is a specific type of abstraction-based summarization that involves using natural language processing techniques to analyze the meaning and structure of the original text and generate a summary that is coherent and natural-sounding. This type of summarization requires a deep understanding of the content and context of the original text, as well as the ability to write in a clear and concise manner.
Automatic summarizer is a software tool or system that is designed to automatically generate a summary of a text. Automatic summarizers can use either extractive or abstractive methods, depending on the specific goals and requirements of the summarization task. Extractive summarizers select a subset of the most important sentences or phrases from the original text and concatenate them to form the summary, while abstractive summarizers build an internal semantic representation of the content and use natural language generation techniques to create a summary that is closer to what a human might write. Automatic summarizers can be used in a variety of contexts, such as to generate summaries of news articles, scientific papers, or other long-form texts, or to create summaries of spoken language, such as transcripts of interviews or lectures.

Wikipedia:

References:

Proceedings of the Workshop on Automatic Text Summarization 2011

See also:

Machine Reading

Toward abstractive summarization using semantic representations
F Liu, J Flanigan, S Thomson, N Sadeh… – arXiv preprint arXiv …, 2018 – arxiv.org
We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into …

Faithful to the original: Fact aware neural abstractive summarization
Z Cao, F Wei, W Li, S Li – Thirty-Second AAAI Conference on Artificial …, 2018 – aaai.org
Unlike extractive summarization, abstractive summarization has to fuse different parts of the source text, which inclines to create fake facts. Our preliminary study reveals nearly 30% of the outputs from a state-of-the-art neural summarization system suffer from this problem …

Bottom-up abstractive summarization
S Gehrmann, Y Deng, AM Rush – arXiv preprint arXiv:1808.10792, 2018 – arxiv.org
Neural network-based methods for abstractive summarization produce outputs that are more fluent than other techniques, but which can be poor at content selection. This work proposes a simple technique for addressing this issue: use a data-efficient content selector to over …

Multimodal abstractive summarization of open-domain videos
J Libovický, S Palaskar, S Gella… – Proceedings of the …, 2018 – nips2018vigil.github.io
Multimodal and abstractive summarization of open-domain videos requires summarizing the contents of an entire video in a few short sentences, while fusing information from multiple modalities, in our case video and audio (or text). Different from traditional news …

A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
A Cohan, F Dernoncourt, DS Kim, T Bui, S Kim… – arXiv preprint arXiv …, 2018 – arxiv.org
Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (eg, research papers). Our approach consists of a new …

Global Encoding for Abstractive Summarization
J Lin, X Sun, S Ma, Q Su – arXiv preprint arXiv:1805.03989, 2018 – arxiv.org
In neural abstractive summarization, the conventional sequence-to-sequence (seq2seq) model often suffers from repetition and semantic irrelevance. To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder …

Deep communicating agents for abstractive summarization
A Celikyilmaz, A Bosselut, X He, Y Choi – arXiv preprint arXiv:1803.10357, 2018 – arxiv.org
We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple …

Structure-Infused Copy Mechanisms for Abstractive Summarization
K Song, L Zhao, F Liu – arXiv preprint arXiv:1806.05658, 2018 – arxiv.org
Seq2seq learning has produced promising results on summarization. However, in many cases, system summaries still struggle to keep the meaning of the original intact. They may miss out important words or relations that play critical roles in the syntactic structure of …

Actor-critic based training framework for abstractive summarization
P Li, L Bing, W Lam – arXiv preprint arXiv:1803.11070, 2018 – arxiv.org
We present a training framework for neural abstractive summarization based on actor-critic approaches from reinforcement learning. In the traditional neural network based methods, the objective is only to maximize the likelihood of the predicted summaries, no other …

Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
B Kim, H Kim, G Kim – arXiv preprint arXiv:1811.00783, 2018 – arxiv.org
We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts from the online discussion forum Reddit. We use such informal crowd-generated posts as …

Neural Abstractive Summarization
S Singhal, A Vats, H Karnick – iitk.ac.in
Text Summarization is an important and hard problem towards machine’s understanding of language. It would give us the ability to process more information in a less time. The two dominant strategies for text summarization are extractive and abstractive summarization …

Hybrid Approach To Abstractive Summarization
D Sahoo, A Bhoi, RC Balabantaray – Procedia Computer Science, 2018 – Elsevier
Text summarization is an application of information retrieval where short and non-redundant version of comparatively large text is presented to the end user. In this paper a hybrid approach is presented to generate abstract summary in which sentences are clustered using …

Unsupervised Semantic Abstractive Summarization
S Dohare, V Gupta, H Karnick – Proceedings of ACL 2018, Student …, 2018 – aclweb.org
Automatic abstractive summary generation remains a significant open problem for natural language processing. In this work, we develop a novel pipeline for Semantic Abstractive Summarization (SAS). SAS, as introduced by Liu et al.(2015) first generates an AMR graph …

Abstractive Summarization using Graph Based Methods
C Badgujar, V Jethani… – 2018 Second International …, 2018 – ieeexplore.ieee.org
In the era of big data, resources are bountiful. Text summarization is a necessary and research area that minifies text such that repeated data are removed and important information is extracted and represented in the concise way which can help us to …

Query-Biased Multi-Document Abstractive Summarisation
A Singhal, A Jindal – iitk.ac.in
In this paper, we introduce a novel pipeline to implement a query-biased multidocument summarisation using abstractive summarisation. Traditional information retrieval systems return a ranked list of whole documents as the answer to a query. However, in many cases …

Model copying and rewriting in neural abstractive summarization
Z Cao – 2018 – ira.lib.polyu.edu.hk
Our research consists of three parts. In the work to be presented in Chapter 3, we leverage the popular attention mechanism to copy and rewrite words in the source text. Our model fuses a copying decoder and a rewriting decoder. The copying decoder finds out words to …

Abstractive Summarization Using Attentive Neural Techniques
J Krantz, J Kalita – arXiv preprint arXiv:1810.08838, 2018 – arxiv.org
In a world of proliferating data, the ability to rapidly summarize text is growing in importance. Automatic summarization of text can be thought of as a sequence to sequence problem. Another area of natural language processing that solves a sequence to sequence problem …

Graph-Based Abstractive Summarization: Compression of Semantic Graphs
B Jagan, R Parthasarathi, TV Geetha – … , and Applications of Semantic …, 2018 – igi-global.com
Customization of information from web documents is an immense job that involves mainly the shortening of original texts. Extractive methods use surface level and statistical features for the selection of important sentences. In contrast, abstractive methods need a formal …

Unsupervised Neural Multi-document Abstractive Summarization
E Chu, PJ Liu – arXiv preprint arXiv:1810.05739, 2018 – arxiv.org
Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some …

Entity Commonsense Representation for Neural Abstractive Summarization
RK Amplayo, S Lim, S Hwang – arXiv preprint arXiv:1806.05504, 2018 – arxiv.org
A major proportion of a text summary includes important entities found in the original text. These entities build up the topic of the summary. Moreover, they hold commonsense information once they are linked to a knowledge base. Based on these observations, this …

A Hybrid Word-Character Model for Abstractive Summarization
CT Chang, CC Huang, JYJ Hsu – arXiv preprint arXiv:1802.09968, 2018 – arxiv.org
Abstractive summarization is the popular research topic nowadays. Due to the difference in language property, Chinese summarization also gains lots of attention. Most of studies use character-based representation instead of word-based to keep out the error introduced by …

Abstractive Summarization with the Aid of Extractive Summarization
Y Chen, Y Ma, X Mao, Q Li – Asia-Pacific Web (APWeb) and Web-Age …, 2018 – Springer
Currently the abstractive method and extractive method are two main approaches for automatic document summarization. To fully integrate the relatedness and advantages of both approaches, we propose in this paper a general framework for abstractive …

Frustratingly Easy Model Ensemble for Abstractive Summarization
H Kobayashi – Proceedings of the 2018 Conference on Empirical …, 2018 – aclweb.org
Ensemble methods, which combine multiple models at decoding time, are now widely known to be effective for text-generation tasks. However, they generally increase computational costs, and thus, there have been many studies on compressing or distilling …

Automatic Abstractive Summarization Task for New Article
A Helen – EMITTER International Journal of Engineering …, 2018 – emitter.pens.ac.id
Understanding the contents of numerous documents requires strenuous effort. While manually reading the summary or abstract is one way, automatic summarization offers more efficient way in doing so. The current research in automatic summarization focuses on the …

A Hierarchical Neural Abstractive Summarization with Self-Attention Mechanism
WJ Yang, ZC Tang, XH Tang – 2018 3rd International Conference …, 2018 – atlantis-press.com
Recently, the attentional seq2seq model had made a remarkable progress on the abstractive summarization. But most of these models do not considers the relation between original sentences, which is the important feature in extractive method. In this work, we …

Abstractive Summarization Improved by WordNet-Based Extractive Sentences
N Xie, S Li, H Ren, Q Zhai – … on Natural Language Processing and Chinese …, 2018 – Springer
Recently, the seq2seq abstractive summarization models have achieved good results on the CNN/Daily Mail dataset. Still, how to improve abstractive methods with extractive methods is a good research direction, since extractive methods have their potentials of exploiting …

Extractive-abstractive summarization with pointer and coverage mechanism
Y Zhang, E Chen, W Xiao – … of 2018 International Conference on Big …, 2018 – dl.acm.org
Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization. However, they are facing the challenges of low efficiency and accuracy when dealing with long text: their capability are not enough to handle very long input, they …

Towards a New Hybrid Approach for Abstractive Summarization
Y Jaafar, K Bouzoubaa – Procedia computer science, 2018 – Elsevier
With the huge amount of Arabic digital data, a summarization system is very helpful to quickly retrieve useful information and save a lot of time and efforts. Two main techniques are used when developing such system: 1) Extractive techniques which consist of returning …

Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting
YC Chen, M Bansal – arXiv preprint arXiv:1805.11080, 2018 – arxiv.org
Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (ie, compresses and paraphrases) to generate a concise overall summary. We use a novel …

Unsupervised Neural Multi-Document Abstractive Summarization of Reviews
E Chu, PJ Liu – 2018 – openreview.net
Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some …

Diverse Beam Search for Increased Novelty in Abstractive Summarization
A Cibils, C Musat, A Hossman, M Baeriswyl – arXiv preprint arXiv …, 2018 – arxiv.org
Text summarization condenses a text to a shorter version while retaining the important informations. Abstractive summarization is a recent development that generates new phrases, rather than simply copying or rephrasing sentences within the original text …

Abstractive summarization by neural attention model with document content memory
Y Choi, D Kim, JH Lee – Proceedings of the 2018 Conference on …, 2018 – dl.acm.org
In this paper, we propose a generative approach for abstractive summarization, which creates summaries based on a language model. The main goal of our paper is to generate a long sequence of words with coherent sentences by reflecting the key concepts of the …

Combined Objective Function in Deep Learning Model for Abstractive Summarization
T Le, N Le Minh – Proceedings of the Ninth International Symposium on …, 2018 – dl.acm.org
Abstractive Summarization is the specific task in text generation whose popular approaches are based on the strength of Recurrent Neural Network. With the purpose to take advantages of Convolution Neural Network in text representation, we propose to combine …

Attention Neural Network-Based Abstractive Summarization and Headline Generation
N Douma – 2018 – isl.anthropomatik.kit.edu
With the ever-increasing frequency of daily content creation, the need for content curators or summarizers is only at its beginning. Manually performing these tasks drive massive costs, thus the increasing demand for efficient automatic summarization systems. Sequence-to …

Robust Neural Abstractive Summarization Systems and Evaluation against Adversarial Information
L Fan, D Yu, L Wang – arXiv preprint arXiv:1810.06065, 2018 – arxiv.org
Sequence-to-sequence (seq2seq) neural models have been actively investigated for abstractive summarization. Nevertheless, existing neural abstractive systems frequently generate factually incorrect summaries and are vulnerable to adversarial information …

Controlling Length in Abstractive Summarization Using a Convolutional Neural Network
Y Liu, Z Luo, K Zhu – Proceedings of the 2018 Conference on Empirical …, 2018 – aclweb.org
Convolutional neural networks (CNNs) have met great success in abstractive summarization, but they cannot effectively generate summaries of desired lengths. Because generated summaries are used in difference scenarios which may have space or length …

Diffusion of Abstractive Summarisation to Improve Ease of Use and Usefulness
P Makovhololo, F Taylor, T Iyamu – 2018 Open Innovations …, 2018 – ieeexplore.ieee.org
Even though the abstractive summarisation method has been in existence for over four decades, its popularity, adoption and diffusion have been slow, or limited, in both business and academic domains. This has retarded advances of the innovation and hampered its …

Improving Pointer-Generator Network with Keywords Information for Chinese Abstractive Summarization
X Jiang, P Hu, L Hou, X Wang – CCF International Conference on Natural …, 2018 – Springer
Recently sequence-to-sequence (Seq2Seq) model and its variants are widely used in multiple summarization tasks eg, sentence compression, headline generation, single document summarization, and have achieved significant performance. However, most of the …

Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation
A Vlachos – arXiv preprint arXiv:1808.09160, 2018 – arxiv.org
Recent work on abstractive summarization has made progress with neural encoder-decoder architectures. However, such models are often challenged due to their lack of explicit semantic modeling of the source document and its summary. In this paper, we extend …

Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation
H Hardy, A Vlachos – Proceedings of the 2018 Conference on Empirical …, 2018 – aclweb.org
Recent work on abstractive summarization has made progress with neural encoder-decoder architectures. However, such models are often challenged due to their lack of explicit semantic modeling of the source document and its summary. In this paper, we extend …

Abstractive Summarization: An Overview of the State of the Art
S Gupta, SK Gupta – Expert Systems with Applications, 2018 – Elsevier
Summarization, is to reduce the size of the document while preserving the meaning, is one of the most researched areas among the Natural Language Processing (NLP) community. Summarization techniques, on the basis of whether the exact sentences are considered as …

A Normalized Encoder-Decoder Model for Abstractive Summarization Using Focal Loss
Y Shi, J Meng, J Wang, H Lin, Y Li – CCF International Conference on …, 2018 – Springer
Abstractive summarization based on seq2seq model is a popular research topic today. And pre-trained word embedding is a common unsupervised method to improve deep learning model’s performance in NLP. However, during applying this method directly to the seq2seq …

A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss
WT Hsu, CK Lin, MY Lee, K Min, J Tang… – arXiv preprint arXiv …, 2018 – arxiv.org
We propose a unified model combining the strength of extractive and abstractive summarization. On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable. On the other hand, a more complicated …

Bidirectional Attentional Encoder-Decoder Model and Bidirectional Beam Search for Abstractive Summarization
K Al-Sabahi, Z Zuping, Y Kang – arXiv preprint arXiv:1809.06662, 2018 – arxiv.org
Sequence generative models with RNN variants, such as LSTM, GRU, show promising performance on abstractive document summarization. However, they still have some issues that limit their performance, especially while deal-ing with long sequences. One of the issues …

Development Of Integrated Framework For Automated Opinion Mining System With Abstractive Summarization
H Akkineni – 2018 – baadalsg.inflibnet.ac.in
In this subchapter, we investigate the procedure of extracting data related to government policies from Twitter using wrapper development and streaming API. Retrieving structured data flowing out of the deep web is the main problem owing to the essentially convoluted …

Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models
T Baumel, M Eyal, M Elhadad – arXiv preprint arXiv:1801.07704, 2018 – arxiv.org
Query Focused Summarization (QFS) has been addressed mostly using extractive methods. Such methods, however, produce text which suffers from low coherence. We investigate how abstractive methods can be applied to QFS, to overcome such limitations. Recent …

Summarisation Of Long Text Extracted From Article Images By Integrating Extractive And Abstractive Text Summarisation …
J Balagopal – Journal of Global Research in Computer Science, 2018 – jgrcs.info
… Keywords: Extractive summarisation, Abstractive summarisation, Parzen-window density function, LexRank, Encoder-Decoder Model, Attention Mechanism … Abstractive summarisation retrieves informa- tion from documents to generate precise summary of information …

Generative adversarial network for abstractive text summarization
L Liu, Y Lu, M Yang, Q Qu, J Zhu, H Li – Thirty-Second AAAI Conference on …, 2018 – aaai.org
… for abstrac- tive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In par- ticular, we build the generator G as an agent of reinforce- ment learning, which takes the raw text as input and predicts the abstractive summarization …

Abstractive Document Summarization via Bidirectional Decoder
X Wan, C Li, R Wang, D Xiao, C Shi – International Conference on …, 2018 – Springer
… Keywords. Abstractive summarization Bidirectional decoder Attention mechanism Sequence-to-sequence architecture. Download conference paper PDF … To solve this problem, we proposed an abstractive Summarization model based on Bidirectional decoder (BiSum), and …

Aspect and sentiment aware abstractive review summarization
M Yang, Q Qu, Y Shen, Q Liu, W Zhao… – Proceedings of the 27th …, 2018 – aclweb.org
… Inspired by recent success of sequence-to-sequence (seq2seq) model in statistical machine translation, most abstractive summarization systems employ seq2seq framework to generate summaries (Nallapati et al., 2016; See et al., 2017; Paulus et al., 2017) …

A Joint Selective Mechanism for Abstractive Sentence Summarization
J Fu, G Liu – Asian Conference on Machine Learning, 2018 – proceedings.mlr.press
… Editors: Jun Zhu and Ichiro Takeuchi Abstract Sequence-to-sequence (Seq2Seq) learning framework has been widely used in many natural language processing (NLP) tasks, including abstractive summarization and machine trans- lation (MT) …

A Model for Automatic Abstractive Multidocument Summarization
R eltayb Ahmed, YJ Kumar – ijcstjournal.org
… ABSTRACT We propose a model for multi document abstractive summarization based on Semantic Role Labeling (SRL (in which the content of the summary is not from the source document but from the semantic representation of the source document …

Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts
CW Goo, YN Chen – arXiv preprint arXiv:1809.05715, 2018 – arxiv.org
… chen@ieee.org ABSTRACT Neural abstractive summarization has been increasingly stud- ied, where the prior work mainly focused on summariz- ing single-speaker documents (news, scientific publications, etc). In dialogues …

Survey on Abstractive Text Summarization
N Raphal, H Duwarah, P Daniel – … International Conference on …, 2018 – ieeexplore.ieee.org
… The second stage is an abstractive summarization model which generate the output based on the first stage extraction. Text Rank, tf-idf, Sumbasic, Cheating are the technique used in the first phase … It uses both extractive and abstractive summarization …

Towards a Neural Network Approach to Abstractive Multi-Document Summarization
J Zhang, J Tan, X Wan – arXiv preprint arXiv:1804.09010, 2018 – arxiv.org
… Abstract Till now, neural abstractive summarization methods have achieved great success for single document summarization (SDS) … In this paper, we investigate neural abstractive methods for MDS by adapting a state-of-the-art neural abstractive summarization model for SDS …

Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study
J Zhang, J Tan, X Wan – … of the 11th International Conference on Natural …, 2018 – aclweb.org
… Abstract Till now, neural abstractive summarization methods have achieved great success for single document summarization (SDS) … In this paper, we investigate neural abstractive methods for MDS by adapting a state-of-the-art neural abstractive summarization model for SDS …

Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion
MT Nayeem, TA Fuad, Y Chali – … of the 27th International Conference on …, 2018 – aclweb.org
… Abstract In this work, we aim at developing an unsupervised abstractive summarization system in the multi-document setting … There are two types of summarizations: abstractive summarization and extractive summarization …

Abstractive Text Summarization by Incorporating Reader Comments
S Gao, X Chen, P Li, Z Ren, L Bing, D Zhao… – arXiv preprint arXiv …, 2018 – arxiv.org
… inc.com Abstract In neural abstractive summarization field, conventional sequence-to-sequence based models often suffer from sum- marizing the wrong aspect of the document with respect to the main aspect. To tackle this …

Ensure the correctness of the summary: incorporate entailment knowledge into abstractive sentence summarization
H Li, J Zhu, J Zhang, C Zong – … of the 27th International Conference on …, 2018 – aclweb.org
… Considering a correct summary is seman- tically entailed by the source sentence, we incorporate entailment knowledge into abstractive summarization models … 8 Conclusion This paper investigates the correctness problem in abstractive summarization …

Abstractive Tabular Dataset Summarization via Knowledge BaseSemantic Embeddings
P Azunre, C Corcoran, D Sullivan, G Honke… – arXiv preprint arXiv …, 2018 – arxiv.org
… first.name}@newknowledge.io ABSTRACT is paper describes an abstractive summarization method1 for tabular data which employs a knowledge base semantic embedding to generate the summary. Assuming the dataset …

Cross-domain Aspect/Sentiment-aware Abstractive Review Summarization
M Yang, Q Qu, J Zhu, Y Shen, Z Zhao – Proceedings of the 27th ACM …, 2018 – dl.acm.org
… 2 RELATED WORK Inspired by the recent success of the encoder-decoder framework in statistical machine translation, there has been increasing interest in generalizing the neural language model to the field of abstractive summarization [7, 8, 10, 11]. For example, Rush et al …

A reinforced topic-aware convolutional sequence-to-sequence model for abstractive text summarization
L Wang, J Yao, Y Tao, L Zhong, W Liu, Q Du – arXiv preprint arXiv …, 2018 – arxiv.org
… We carry out the experimen- tal evaluation with state-of-the-art methods over the Gigaword, DUC-2004, and LCSTS datasets. The empirical results demonstrate the superiority of our proposed method in the abstractive summarization …

Cross-domain aspect/sentiment-aware abstractive review summarization by combining topic modeling and deep reinforcement learning
M Yang, Q Qu, Y Shen, K Lei, J Zhu – Neural Computing and Applications – Springer
… source text. Inspired by the recent success of sequence-to-sequence (seq2seq) model in statistical machine translation, most abstractive summarization systems employ seq2seq framework to generate summaries [29, 32, 37]. In …

Abstractive Text Summarization based on Improved Semantic Graph Approach
A Khan, N Salim, H Farman, M Khan, B Jan… – International Journal of …, 2018 – Springer
… Abstract The goal of abstractive summarization of multi-documents is to automati- cally produce a condensed version of the document text and maintain the significant information … 123 Page 4. Int J Parallel Prog marization and single document abstractive summarization …

Dual encoding for abstractive text summarization
K Yao, L Zhang, D Du, T Luo, L Tao… – IEEE transactions on …, 2018 – ieeexplore.ieee.org
… source text. Different from extractive methods copying units from the source article directly, abstractive summarization uses the read- able language for human to summarize the key information of the original text. Therefore, abstractive …

Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks
C Khatri, G Singh, N Parikh – arXiv preprint arXiv:1807.08000, 2018 – arxiv.org
… Following are the main contributions of our work:- • Obtaining the context vectors from documents, which can be used for extractive and abstractive summarization tasks … Abstractive summarization techniques are less prevalent in the literature than the extractive ones …

Domain-Aware Abstractive Text Summarization for Medical Documents
P Gigioli, N Sagar, J Voyles… – 2018 IEEE International …, 2018 – ieeexplore.ieee.org
… Our work extends neural abstractive techniques to the biomedical domain to create a domain-specific, abstractive summarization system that is capable of generating novel summaries while being aware of domain knowledge …

Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network
C Li, W Xu, S Li, S Gao – Proceedings of the 2018 Conference of the …, 2018 – aclweb.org
… Recently, inspired by the success of encoder- decoder model (Sutskever et al., 2014), abstractive summarization models (Nallapati et al., 2016; See et al., 2017) are able to generate the summaries with high ROUGE scores …

Improving Neural Abstractive Document Summarization with Structural Regularization
W Li, X Xiao, Y Lyu, Y Wang – Proceedings of the 2018 Conference on …, 2018 – aclweb.org
… Recent success of neural sequence-to-sequence (seq2seq) architecture on text generation tasks like ma- chine translation (Bahdanau et al., 2014) and im- age caption (Vinyals et al., 2015), has attracted growing attention to abstractive summarization research …

Deep recurrent neural networks for abstractive text summarization
M Klönne – 2018 – elib.dlr.de
… However, the occurrence of repetitive word sequences and finding the right context is still a problem which needs to be solved. The benefit of a abstractive summarization model becomes apparent when we look at the amount of texts that circulate worldwide …

Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
G Shang, W Ding, Z Zhang, AJP Tixier… – arXiv preprint arXiv …, 2018 – arxiv.org
… In addition to the aforementioned contributions, we also introduce the following novel components into our abstractive summarization pipeline: • we inject global exterior knowledge into the edge weights of the MSCG, by using the Word At- traction Force of Wang et al …

Abstractive text summarization with attention-based mechanism
N Sanjabi – 2018 – upcommons.upc.edu
… compose them again in a summary. Indeed the abstractive summarization is very … Page 8. 2 Chapter 1. Introduction were fitting in the extractive method the available algorithms, tools and resources were not sufficient to make an abstractive summarization systems …

Abstractive Text Summarization of the Parkland Shooting Collection
R Kingery, SR Yellapantula, C Xu, LJ Huang, J Ye – 2018 – vtechworks.lib.vt.edu
… To summarize each individual article we explore various state of the art deep learning methods for abstractive summarization: a sequence-to-sequence model, a pointer generator network, and a reinforced extractor-abstractor network …

Neural Abstractive Text Summarization with Sequence-to-Sequence Models
T Shi, Y Keneshloo, N Ramakrishnan… – arXiv preprint arXiv …, 2018 – arxiv.org
… Celikyilmaz et al. [18] introduced a novel deep communicating agents method for abstractive summarization, where they also adopted the RL loss in their objective function. Pasunuru et al. [57] applied the self-critic policy gradient algorithm to train the pointer-generator network …

Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling
W Li, X Xiao, Y Lyu, Y Wang – Proceedings of the 2018 Conference on …, 2018 – aclweb.org
Page 1. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1787–1796 Brussels, Belgium, October 31 – November 4, 2018. c 2018 Association for Computational Linguistics 1787 …

The Rule of Three: Abstractive Text Summarization in Three Bullet Points
T Kodaira, M Komachi – arXiv preprint arXiv:1809.10867, 2018 – arxiv.org
Page 1. The Rule of Three: Abstractive Text Summarization in Three Bullet Points Tomonori Kodaira kdktmk@gmail.com Graduate School of System Design Tokyo Metropolitan University Mamoru Komachi komachi@tmu.ac.jp …

Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN
J Chen, H Zhuge – Proceedings of the 2018 Conference on Empirical …, 2018 – aclweb.org
Page 1. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4046–4056 Brussels, Belgium, October 31 – November 4, 2018. c 2018 Association for Computational Linguistics 4046 Abstract …

Deep Reinforcement Learning and Generative Adversarial Networks for Abstractive Text Summarization
BR Lie, ANH Kalmar – 2018 – brage.bibsys.no
Page 1. Deep Reinforcement Learning and Generative Adversarial Networks for Abstractive Text Summarization Alf Niklas Håkonsen Kalmar Borgar Rannem Lie Master of Science in Computer Science Supervisor: Massimiliano Ruocco, IDI Co-supervisor: Erlend Aune, Exabel …

Training Neural Models for Abstractive Text Summarization
W Kry?ci?ski – 2018 – diva-portal.org
Page 1. IN DEGREE PROJECT COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS , STOCKHOLM SWEDEN 2018 Training Neural Models for Abstractive Text Summarization WOJCIECH KRYSCINSKI …