@inproceedings{cho-etal-2020-better,
title = "Better Highlighting: Creating Sub-Sentence Summary Highlights",
author = "Cho, Sangwoo and
Song, Kaiqiang and
Li, Chen and
Yu, Dong and
Foroosh, Hassan and
Liu, Fei",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.509",
doi = "10.18653/v1/2020.emnlp-main.509",
pages = "6282--6300",
abstract = "Amongst the best means to summarize is highlighting. In this paper, we aim to generate summary highlights to be overlaid on the original documents to make it easier for readers to sift through a large amount of text. The method allows summaries to be understood in context to prevent a summarizer from distorting the original meaning, of which abstractive summarizers usually fall short. In particular, we present a new method to produce self-contained highlights that are understandable on their own to avoid confusion. Our method combines determinantal point processes and deep contextualized representations to identify an optimal set of sub-sentence segments that are both important and non-redundant to form summary highlights. To demonstrate the flexibility and modeling power of our method, we conduct extensive experiments on summarization datasets. Our analysis provides evidence that highlighting is a promising avenue of research towards future summarization.",
}
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<abstract>Amongst the best means to summarize is highlighting. In this paper, we aim to generate summary highlights to be overlaid on the original documents to make it easier for readers to sift through a large amount of text. The method allows summaries to be understood in context to prevent a summarizer from distorting the original meaning, of which abstractive summarizers usually fall short. In particular, we present a new method to produce self-contained highlights that are understandable on their own to avoid confusion. Our method combines determinantal point processes and deep contextualized representations to identify an optimal set of sub-sentence segments that are both important and non-redundant to form summary highlights. To demonstrate the flexibility and modeling power of our method, we conduct extensive experiments on summarization datasets. Our analysis provides evidence that highlighting is a promising avenue of research towards future summarization.</abstract>
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%0 Conference Proceedings
%T Better Highlighting: Creating Sub-Sentence Summary Highlights
%A Cho, Sangwoo
%A Song, Kaiqiang
%A Li, Chen
%A Yu, Dong
%A Foroosh, Hassan
%A Liu, Fei
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F cho-etal-2020-better
%X Amongst the best means to summarize is highlighting. In this paper, we aim to generate summary highlights to be overlaid on the original documents to make it easier for readers to sift through a large amount of text. The method allows summaries to be understood in context to prevent a summarizer from distorting the original meaning, of which abstractive summarizers usually fall short. In particular, we present a new method to produce self-contained highlights that are understandable on their own to avoid confusion. Our method combines determinantal point processes and deep contextualized representations to identify an optimal set of sub-sentence segments that are both important and non-redundant to form summary highlights. To demonstrate the flexibility and modeling power of our method, we conduct extensive experiments on summarization datasets. Our analysis provides evidence that highlighting is a promising avenue of research towards future summarization.
%R 10.18653/v1/2020.emnlp-main.509
%U https://aclanthology.org/2020.emnlp-main.509
%U https://doi.org/10.18653/v1/2020.emnlp-main.509
%P 6282-6300
Markdown (Informal)
[Better Highlighting: Creating Sub-Sentence Summary Highlights](https://aclanthology.org/2020.emnlp-main.509) (Cho et al., EMNLP 2020)
ACL
- Sangwoo Cho, Kaiqiang Song, Chen Li, Dong Yu, Hassan Foroosh, and Fei Liu. 2020. Better Highlighting: Creating Sub-Sentence Summary Highlights. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6282–6300, Online. Association for Computational Linguistics.