@inproceedings{dohare-etal-2018-unsupervised,
title = "Unsupervised Semantic Abstractive Summarization",
author = "Dohare, Shibhansh and
Gupta, Vivek and
Karnick, Harish",
editor = "Shwartz, Vered and
Tabassum, Jeniya and
Voigt, Rob and
Che, Wanxiang and
de Marneffe, Marie-Catherine and
Nissim, Malvina",
booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-3011",
doi = "10.18653/v1/P18-3011",
pages = "74--83",
abstract = "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 of an input story, through which it extracts a summary graph and finally, creates summary sentences from this summary graph. Compared to earlier approaches, we develop a more comprehensive method to generate the story AMR graph using state-of-the-art co-reference resolution and Meta Nodes. Which we then use in a novel unsupervised algorithm based on how humans summarize a piece of text to extract the summary sub-graph. Our algorithm outperforms the state of the art SAS method by 1.7{\%} F1 score in node prediction.",
}
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<abstract>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 of an input story, through which it extracts a summary graph and finally, creates summary sentences from this summary graph. Compared to earlier approaches, we develop a more comprehensive method to generate the story AMR graph using state-of-the-art co-reference resolution and Meta Nodes. Which we then use in a novel unsupervised algorithm based on how humans summarize a piece of text to extract the summary sub-graph. Our algorithm outperforms the state of the art SAS method by 1.7% F1 score in node prediction.</abstract>
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%0 Conference Proceedings
%T Unsupervised Semantic Abstractive Summarization
%A Dohare, Shibhansh
%A Gupta, Vivek
%A Karnick, Harish
%Y Shwartz, Vered
%Y Tabassum, Jeniya
%Y Voigt, Rob
%Y Che, Wanxiang
%Y de Marneffe, Marie-Catherine
%Y Nissim, Malvina
%S Proceedings of ACL 2018, Student Research Workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F dohare-etal-2018-unsupervised
%X 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 of an input story, through which it extracts a summary graph and finally, creates summary sentences from this summary graph. Compared to earlier approaches, we develop a more comprehensive method to generate the story AMR graph using state-of-the-art co-reference resolution and Meta Nodes. Which we then use in a novel unsupervised algorithm based on how humans summarize a piece of text to extract the summary sub-graph. Our algorithm outperforms the state of the art SAS method by 1.7% F1 score in node prediction.
%R 10.18653/v1/P18-3011
%U https://aclanthology.org/P18-3011
%U https://doi.org/10.18653/v1/P18-3011
%P 74-83
Markdown (Informal)
[Unsupervised Semantic Abstractive Summarization](https://aclanthology.org/P18-3011) (Dohare et al., ACL 2018)
ACL
- Shibhansh Dohare, Vivek Gupta, and Harish Karnick. 2018. Unsupervised Semantic Abstractive Summarization. In Proceedings of ACL 2018, Student Research Workshop, pages 74–83, Melbourne, Australia. Association for Computational Linguistics.