StructSum: Summarization via Structured Representations

• URL: http://arxiv.org/abs/2003.00576v2
• Date: Tue, 16 Feb 2021 18:59:36 GMT
• Title: StructSum: Summarization via Structured Representations
• Authors: Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
• Abstract summary: Abstractive text summarization aims at compressing the information of a long source document into a condensed summary. Despite advances in modeling techniques, abstractive summarization models still suffer from several key challenges. We propose a framework based on document-level structure induction for summarization to address these challenges.
• Score: 27.890477913486787
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Abstractive text summarization aims at compressing the information of a long source document into a rephrased, condensed summary. Despite advances in modeling techniques, abstractive summarization models still suffer from several key challenges: (i) layout bias: they overfit to the style of training corpora; (ii) limited abstractiveness: they are optimized to copying n-grams from the source rather than generating novel abstractive summaries; (iii) lack of transparency: they are not interpretable. In this work, we propose a framework based on document-level structure induction for summarization to address these challenges. To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models. Our framework complements standard encoder-decoder summarization models by augmenting them with rich structure-aware document representations based on implicitly learned (latent) structures and externally-derived linguistic (explicit) structures. We show that our summarization framework, trained on the CNN/DM dataset, improves the coverage of content in the source documents, generates more abstractive summaries by generating more novel n-grams, and incorporates interpretable sentence-level structures, while performing on par with standard baselines.

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