Unsupervised Semantic Abstractive Summarization

Shibhansh Dohare, Vivek Gupta, Harish Karnick


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.
Anthology ID:
P18-3011
Volume:
Proceedings of ACL 2018, Student Research Workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
74–83
Language:
URL:
https://aclanthology.org/P18-3011
DOI:
10.18653/v1/P18-3011
Bibkey:
Cite (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.
Cite (Informal):
Unsupervised Semantic Abstractive Summarization (Dohare et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/P18-3011.pdf
Code
 shibhansh/Unsupervised-SAS
Data
AMR Bank