Topic-Guided Abstractive Multi-Document Summarization

• URL: http://arxiv.org/abs/2110.11207v1
• Date: Thu, 21 Oct 2021 15:32:30 GMT
• Title: Topic-Guided Abstractive Multi-Document Summarization
• Authors: Peng Cui, Le Hu
• Abstract summary: A critical point of multi-document summarization (MDS) is to learn the relations among various documents. We propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneous graph. We employ a neural topic model to jointly discover latent topics that can act as cross-document semantic units.
• Score: 21.856615677793243
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A critical point of multi-document summarization (MDS) is to learn the relations among various documents. In this paper, we propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneous graph, taking semantic nodes of different granularities into account, and then apply a graph-to-sequence framework to generate summaries. Moreover, we employ a neural topic model to jointly discover latent topics that can act as cross-document semantic units to bridge different documents and provide global information to guide the summary generation. Since topic extraction can be viewed as a special type of summarization that "summarizes" texts into a more abstract format, i.e., a topic distribution, we adopt a multi-task learning strategy to jointly train the topic and summarization module, allowing the promotion of each other. Experimental results on the Multi-News dataset demonstrate that our model outperforms previous state-of-the-art MDS models on both Rouge metrics and human evaluation, meanwhile learns high-quality topics.

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