Abstract
Conventional solutions to automatic related work summarization rely heavily on human-engineered features. In this paper, we develop a neural data-driven summarizer by leveraging the seq2seq paradigm, in which a joint context-driven attention mechanism is proposed to measure the contextual relevance within full texts and a heterogeneous bibliography graph simultaneously. Our motivation is to maintain the topic coherency between a related work section and its target document, where both the textual and graphic contexts play a big role in characterizing the relationship among scientific publications accurately. Experimental results on a large dataset show that our approach achieves a considerable improvement over a typical seq2seq summarizer and five classical summarization baselines.- Anthology ID:
- D18-1204
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1776–1786
- Language:
- URL:
- https://aclanthology.org/D18-1204
- DOI:
- 10.18653/v1/D18-1204
- Cite (ACL):
- Yongzhen Wang, Xiaozhong Liu, and Zheng Gao. 2018. Neural Related Work Summarization with a Joint Context-driven Attention Mechanism. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1776–1786, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Neural Related Work Summarization with a Joint Context-driven Attention Mechanism (Wang et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D18-1204.pdf
- Code
- kuadmu/2018EMNLP