Discourse-Aware Hierarchical Attention Network for Extractive Single-Document Summarization
Tatsuya Ishigaki, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura
Abstract
Discourse relations between sentences are often represented as a tree, and the tree structure provides important information for summarizers to create a short and coherent summary. However, current neural network-based summarizers treat the source document as just a sequence of sentences and ignore the tree-like discourse structure inherent in the document. To incorporate the information of a discourse tree structure into the neural network-based summarizers, we propose a discourse-aware neural extractive summarizer which can explicitly take into account the discourse dependency tree structure of the source document. Our discourse-aware summarizer can jointly learn the discourse structure and the salience score of a sentence by using novel hierarchical attention modules, which can be trained on automatically parsed discourse dependency trees. Experimental results showed that our model achieved competitive or better performances against state-of-the-art models in terms of ROUGE scores on the DailyMail dataset. We further conducted manual evaluations. The results showed that our approach also gained the coherence of the output summaries.- Anthology ID:
- R19-1059
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
- Month:
- September
- Year:
- 2019
- Address:
- Varna, Bulgaria
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 497–506
- Language:
- URL:
- https://aclanthology.org/R19-1059
- DOI:
- 10.26615/978-954-452-056-4_059
- Cite (ACL):
- Tatsuya Ishigaki, Hidetaka Kamigaito, Hiroya Takamura, and Manabu Okumura. 2019. Discourse-Aware Hierarchical Attention Network for Extractive Single-Document Summarization. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 497–506, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Discourse-Aware Hierarchical Attention Network for Extractive Single-Document Summarization (Ishigaki et al., RANLP 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/R19-1059.pdf