Unsupervised Multi-document Summarization with Holistic Inference
Haopeng Zhang Sangwoo Cho Kaiqiang Song Xiaoyang Wang Hongwei Wang Jiawei Zhang Dong Yu
Multi-document summarization aims to obtain core information from a
collection of documents written on the same topic. This paper proposes a new
holistic framework for unsupervised multi-document extractive summarization.
Our method incorporates the holistic beam search inference method associated
with the holistic measurements, named Subset Representative Index (SRI). SRI
balances the importance and diversity of a subset of sentences from the source
documents and can be calculated in unsupervised and adaptive manners. To
demonstrate the effectiveness of our method, we conduct extensive experiments
on both small and large-scale multi-document summarization datasets under both
unsupervised and adaptive settings. The proposed method outperforms strong
baselines by a significant margin, as indicated by the resulting ROUGE scores
and diversity measures. Our findings also suggest that diversity is essential
for improving multi-document summary performance.