Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset
This work addresses multi-document summarization for news readers by introducing a new dataset and an incremental model enhancement.
The paper tackled reader-aware multi-document summarization by extending a VAE-based framework to incorporate reader comments, resulting in improved summarization performance as demonstrated experimentally.
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. The annotated dataset for RA-MDS is available online.