Abstract
Single-document and multi-document summarizations are very closely related in both task definition and solution method. In this work, we propose to improve neural abstractive multi-document summarization by jointly learning an abstractive single-document summarizer. We build a unified model for single-document and multi-document summarizations by fully sharing the encoder and decoder and utilizing a decoding controller to aggregate the decoder’s outputs for multiple input documents. We evaluate our model on two multi-document summarization datasets: Multi-News and DUC-04. Experimental results show the efficacy of our approach, and it can substantially outperform several strong baselines. We also verify the helpfulness of single-document summarization to abstractive multi-document summarization task.- Anthology ID:
- 2020.findings-emnlp.231
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2545–2554
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.findings-emnlp.231/
- DOI:
- 10.18653/v1/2020.findings-emnlp.231
- Cite (ACL):
- Hanqi Jin and Xiaojun Wan. 2020. Abstractive Multi-Document Summarization via Joint Learning with Single-Document Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2545–2554, Online. Association for Computational Linguistics.
- Cite (Informal):
- Abstractive Multi-Document Summarization via Joint Learning with Single-Document Summarization (Jin & Wan, Findings 2020)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.findings-emnlp.231.pdf
- Code
- zhongxia96/mds-and-sds
- Data
- Multi-News