Abstract
A critical point of multi-document summarization (MDS) is to learn the relations among various documents. In this paper, we propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneous graph, taking semantic nodes of different granularities into account, and then apply a graph-to-sequence framework to generate summaries. Moreover, we employ a neural topic model to jointly discover latent topics that can act as cross-document semantic units to bridge different documents and provide global information to guide the summary generation. Since topic extraction can be viewed as a special type of summarization that “summarizes” texts into a more abstract format, i.e., a topic distribution, we adopt a multi-task learning strategy to jointly train the topic and summarization module, allowing the promotion of each other. Experimental results on the Multi-News dataset demonstrate that our model outperforms previous state-of-the-art MDS models on both Rouge scores and human evaluation, meanwhile learns high-quality topics.- Anthology ID:
- 2021.findings-emnlp.126
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1463–1472
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.126
- DOI:
- 10.18653/v1/2021.findings-emnlp.126
- Cite (ACL):
- Peng Cui and Le Hu. 2021. Topic-Guided Abstractive Multi-Document Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1463–1472, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Topic-Guided Abstractive Multi-Document Summarization (Cui & Hu, Findings 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.findings-emnlp.126.pdf
- Data
- Multi-News