@inproceedings{lebanoff-etal-2019-scoring,
title = "Scoring Sentence Singletons and Pairs for Abstractive Summarization",
author = "Lebanoff, Logan and
Song, Kaiqiang and
Dernoncourt, Franck and
Kim, Doo Soon and
Kim, Seokhwan and
Chang, Walter and
Liu, Fei",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1209",
doi = "10.18653/v1/P19-1209",
pages = "2175--2189",
abstract = "When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood. Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinations of them. There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs. This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a unified space. Our proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence. We conduct extensive experiments on both single- and multi-document summarization datasets and report findings on sentence selection and abstraction.",
}
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<abstract>When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood. Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinations of them. There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs. This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a unified space. Our proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence. We conduct extensive experiments on both single- and multi-document summarization datasets and report findings on sentence selection and abstraction.</abstract>
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%0 Conference Proceedings
%T Scoring Sentence Singletons and Pairs for Abstractive Summarization
%A Lebanoff, Logan
%A Song, Kaiqiang
%A Dernoncourt, Franck
%A Kim, Doo Soon
%A Kim, Seokhwan
%A Chang, Walter
%A Liu, Fei
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F lebanoff-etal-2019-scoring
%X When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood. Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinations of them. There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs. This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a unified space. Our proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence. We conduct extensive experiments on both single- and multi-document summarization datasets and report findings on sentence selection and abstraction.
%R 10.18653/v1/P19-1209
%U https://aclanthology.org/P19-1209
%U https://doi.org/10.18653/v1/P19-1209
%P 2175-2189
Markdown (Informal)
[Scoring Sentence Singletons and Pairs for Abstractive Summarization](https://aclanthology.org/P19-1209) (Lebanoff et al., ACL 2019)
ACL
- Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, and Fei Liu. 2019. Scoring Sentence Singletons and Pairs for Abstractive Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2175–2189, Florence, Italy. Association for Computational Linguistics.