Scoring sentences in documents given abstract summaries created by humans is important in extractive multi-document summarization. In this paper, we formulate extractive summarization as a two step learning problem building a generative model for pattern discovery and a regression model for inference. We calculate scores for sentences in document clusters based on their latent characteristics using a hierarchical topic model. Then, using these scores, we train a regression model based on the lexical and structural characteristics of the sentences, and use the model to score sentences of new documents to form a summary. Our system advances current state-of-the-art improving ROUGE scores by ∼7%. Generated summaries are less redundant and more coherent based upon manual quality evaluations.
HybHSum REF formulates multi-document summarization as a prediction problem based on a two-step hybrid model: a generative model for hierarchical topic discovery and a regression model for inference.
A Hybrid Hierarchical Model for Multi-Document Summarization
ACL
Computer Science