Abstract
We present a new neural sequence-to-sequence model for extractive summarization called SWAP-NET (Sentences and Words from Alternating Pointer Networks). Extractive summaries comprising a salient subset of input sentences, often also contain important key words. Guided by this principle, we design SWAP-NET that models the interaction of key words and salient sentences using a new two-level pointer network based architecture. SWAP-NET identifies both salient sentences and key words in an input document, and then combines them to form the extractive summary. Experiments on large scale benchmark corpora demonstrate the efficacy of SWAP-NET that outperforms state-of-the-art extractive summarizers.- Anthology ID:
- P18-1014
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 142–151
- Language:
- URL:
- https://aclanthology.org/P18-1014
- DOI:
- 10.18653/v1/P18-1014
- Cite (ACL):
- Aishwarya Jadhav and Vaibhav Rajan. 2018. Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 142–151, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks (Jadhav & Rajan, ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P18-1014.pdf
- Data
- CNN/Daily Mail