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arXiv · 1806.05655

Abstract Meaning Representation for Multi-Document Summarization

Kexin Liao·Logan Lebanoff·Fei Liu
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Abstract

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.

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BibTeXRIS

Kexin Liao, Logan Lebanoff, Fei Liu. 2018-06-14. Abstract Meaning Representation for Multi-Document Summarization. https://arxiv.org/abs/1806.05655

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