Interactive abstractive summarization for event news tweets

Ori Shapira; Hadar Ronen; Meni Adler; Yael Amsterdamer; Judit Bar-Ilan; Ido Dagan

doi:10.18653/v1/d17-2019

Interactive abstractive summarization for event news tweets

Ori Shapira
, Hadar Ronen
, Meni Adler
, Yael Amsterdamer
, Judit Bar-Ilan
, Ido Dagan

Bar-Ilan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

20 Scopus citations

Abstract

We present a novel interactive summarization system that is based on abstractive summarization, derived from a recent consolidated knowledge representation for multiple texts. We incorporate a couple of interaction mechanisms, providing a bullet-style summary while allowing to attain the most important information first and interactively drill down to more specific details. A usability study of our implementation, for event news tweets, suggests the utility of our approach for text exploration.

Original language	English
Title of host publication	EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing
Subtitle of host publication	System Demonstrations, Proceedings
Publisher	Association for Computational Linguistics (ACL)
Pages	109-114
Number of pages	6
ISBN (Electronic)	9781945626975
DOIs	https://doi.org/10.18653/v1/d17-2019
State	Published - 2017
Event	2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2017 - Copenhagen, Denmark Duration: 9 Sep 2017 → 11 Sep 2017

Publication series

Name	EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings

Conference

Conference	2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2017
Country/Territory	Denmark
City	Copenhagen
Period	9/09/17 → 11/09/17

Bibliographical note

Publisher Copyright:
© 2017 Association for Computational Linguistics.

Funding

This work was supported in part by grants from the MAGNET program of the Israeli Office of the Chief Scientist (OCS); the German Research Foundation through the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1); by Contract HR0011-15-2-0025 with the US Defense Advanced Research Projects Agency (DARPA); by the BIU Center for Research in Applied Cryptography and Cyber Security in conjunction with the Israel National Cyber Bureau in the Prime Ministers Office; and by the Israel Science Foundation (grant No. 1157/16).

Funders	Funder number
Israel National Cyber Bureau
Defense Advanced Research Projects Agency
German-Israeli Foundation for Scientific Research and Development	HR0011-15-2-0025, DA 1600/1-1
Israel Science Foundation	1157/16
Office of the Chief Scientist, Ministry of Economy
Center for Research in Applied Cryptography and Cyber Security, Bar-Ilan University

Access to Document

10.18653/v1/d17-2019

Cite this

Shapira, O., Ronen, H., Adler, M., Amsterdamer, Y., Bar-Ilan, J., & Dagan, I. (2017). Interactive abstractive summarization for event news tweets. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings (pp. 109-114). (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d17-2019

Shapira, Ori ; Ronen, Hadar ; Adler, Meni et al. / Interactive abstractive summarization for event news tweets. EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings. Association for Computational Linguistics (ACL), 2017. pp. 109-114 (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings).

@inproceedings{e097e0d057074f1e9047c342f52817a3,

title = "Interactive abstractive summarization for event news tweets",

abstract = "We present a novel interactive summarization system that is based on abstractive summarization, derived from a recent consolidated knowledge representation for multiple texts. We incorporate a couple of interaction mechanisms, providing a bullet-style summary while allowing to attain the most important information first and interactively drill down to more specific details. A usability study of our implementation, for event news tweets, suggests the utility of our approach for text exploration.",

author = "Ori Shapira and Hadar Ronen and Meni Adler and Yael Amsterdamer and Judit Bar-Ilan and Ido Dagan",

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doi = "10.18653/v1/d17-2019",

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Shapira, O, Ronen, H, Adler, M, Amsterdamer, Y, Bar-Ilan, J & Dagan, I 2017, Interactive abstractive summarization for event news tweets. in EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings. EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings, Association for Computational Linguistics (ACL), pp. 109-114, 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2017, Copenhagen, Denmark, 9/09/17. https://doi.org/10.18653/v1/d17-2019

Interactive abstractive summarization for event news tweets. / Shapira, Ori; Ronen, Hadar; Adler, Meni et al.
EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings. Association for Computational Linguistics (ACL), 2017. p. 109-114 (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Shapira O, Ronen H, Adler M, Amsterdamer Y, Bar-Ilan J, Dagan I. Interactive abstractive summarization for event news tweets. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings. Association for Computational Linguistics (ACL). 2017. p. 109-114. (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings). doi: 10.18653/v1/d17-2019

Interactive abstractive summarization for event news tweets

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