Abstract
We investigate the problem of reader-aware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. To conduct evaluation for summarization performance, we prepare a new dataset. We describe the methods for data collection, aspect annotation, and summary writing as well as scrutinizing by experts. Experimental results show that reader comments can improve the summarization performance, which also demonstrates the usefulness of the proposed dataset.- Anthology ID:
- W17-4512
- Volume:
- Proceedings of the Workshop on New Frontiers in Summarization
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 91–99
- Language:
- URL:
- https://aclanthology.org/W17-4512
- DOI:
- 10.18653/v1/W17-4512
- Cite (ACL):
- Piji Li, Lidong Bing, and Wai Lam. 2017. Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset. In Proceedings of the Workshop on New Frontiers in Summarization, pages 91–99, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset (Li et al., 2017)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W17-4512.pdf