Abstract Meaning Representation for Multi-Document Summarization

Kexin Liao, Logan Lebanoff, Fei Liu


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.
Anthology ID:
C18-1101
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1178–1190
Language:
URL:
https://aclanthology.org/C18-1101
DOI:
Bibkey:
Cite (ACL):
Kexin Liao, Logan Lebanoff, and Fei Liu. 2018. Abstract Meaning Representation for Multi-Document Summarization. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1178–1190, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Abstract Meaning Representation for Multi-Document Summarization (Liao et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-1101.pdf