HISum: Hyperbolic Interaction Model for Extractive Multi-Document Summarization

Extractive summarization helps provide a short description or a digest of news or other web texts. It enhances the reading experience of users, especially when they are reading on small displays (e.g., mobile phones). Matching-based methods are recently proposed for the extractive summarization task, which extracts a summary from a global view via a document-summary matching framework. However, these methods only calculate similarities between candidate summaries and the entire document embeddings, insufficiently capturing interactions between different contextual information in the document to accurately estimate the importance of candidates. In this paper, we propose a new hyperbolic interaction model for extractive multi-document summarization (HISum). Specifically, HISum first learns document and candidate summary representations in the same hyperbolic space to capture latent hierarchical structures and then estimates the importance scores of candidates by jointly modeling interactions between each candidate and the document from global and local views. Finally, the importance scores are used to rank and extract the best candidate as the extracted summary. Experimental results on several benchmarks show that HISum outperforms the state-of-the-art extractive baselines1.