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
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1178–1190
- Language:
- URL:
- https://aclanthology.org/C18-1101
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/C18-1101.pdf