Abstract Meaning Representation For Multi-Document Summarization

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.

Publication Date

1-1-2018

Publication Title

COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings

Number of Pages

1178-1190

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

85055608122 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85055608122

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