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abstract

Reinforcement Learning Models for Abstractive Text Summarization

Published: 18 April 2019 Publication History

Abstract

Abstractive text summarization is an active research topic in Natural Language Understanding. We live in a digital world where the information for every topic in Internet is increasing considerable, and users would benefit by generating summaries. Summaries are categorized in the following: summary generated through extractive methods and summary generated through abstractive methods. Abstractive methods are highly complex, due the fact that generates an abstract summary consisting of ideas or concepts listed in the documents but are repeated or interpreted with different words or phrases (models that are not restricted to selecting and rearranging phrases from the original documents). Most of today's tasks in Natural Language Processing (NLP) are performed with semi-supervised or supervised learning models. In this research we are looking to perform the text summarization using a new architecture that take advantage of Pre-trained models and Reinforcement Learning (RL). The goal of the research is to extend the summarization to multiple datasets at the time of training to generate summaries. The evaluation is performed on the CNN/Daily Mail, New York Times and Wikihow datasets.

References

[1]
A. Celikyilmaz, A. Bosselut, X. He, and Y. Choi. 2018. Deep Communicating Agents for Abstractive Summarization.
[2]
Y.-C. Chen and M. Bansal. 2018. Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting.
[3]
R. Paulus, C. Xiong, and R. Socher. 2018. A Deep Reinforced Model for Abstractive Summarization. Openreview.net. https://openreview.net/forum?id=HkAClQgA-.

Cited By

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  • (2023)Preference-controlled multi-objective reinforcement learning for conditional text generationProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i11.26490(12662-12672)Online publication date: 7-Feb-2023
  • (2022)Single and Multi-Documents Text Summarization Technologies for Natural Language Processing: a Systematic Review on Method and Dataset2022 IEEE Creative Communication and Innovative Technology (ICCIT)10.1109/ICCIT55355.2022.10118868(1-7)Online publication date: 22-Nov-2022

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Published In

cover image ACM Conferences
ACMSE '19: Proceedings of the 2019 ACM Southeast Conference
April 2019
295 pages
ISBN:9781450362511
DOI:10.1145/3299815
  • Conference Chair:
  • Dan Lo,
  • Program Chair:
  • Donghyun Kim,
  • Publications Chair:
  • Eric Gamess
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2019

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Author Tags

  1. Abstractive Text Summarization
  2. Reinforcement Learning
  3. Transformers

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  • Abstract
  • Research
  • Refereed limited

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ACM SE '19
Sponsor:
ACM SE '19: 2019 ACM Southeast Conference
April 18 - 20, 2019
GA, Kennesaw, USA

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Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Cited By

View all
  • (2023)Preference-controlled multi-objective reinforcement learning for conditional text generationProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i11.26490(12662-12672)Online publication date: 7-Feb-2023
  • (2022)Single and Multi-Documents Text Summarization Technologies for Natural Language Processing: a Systematic Review on Method and Dataset2022 IEEE Creative Communication and Innovative Technology (ICCIT)10.1109/ICCIT55355.2022.10118868(1-7)Online publication date: 22-Nov-2022

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