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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P18-3011.pdf
- Code
- shibhansh/Unsupervised-SAS
- Data
- AMR Bank