Abstract
This work introduces a new problem, relational summarization, in which the goal is to generate a natural language summary of the relationship between two lexical items in a corpus, without reference to a knowledge base. Motivated by the needs of novel user interfaces, we define the task and give examples of its application. We also present a new query-focused method for finding natural language sentences which express relationships. Our method allows for summarization of more than two times more query pairs than baseline relation extractors, while returning measurably more readable output. Finally, to help guide future work, we analyze the challenges of relational summarization using both a news and a social media corpus.- Anthology ID:
- N18-1159
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1760–1769
- Language:
- URL:
- https://aclanthology.org/N18-1159
- DOI:
- 10.18653/v1/N18-1159
- Cite (ACL):
- Abram Handler and Brendan O’Connor. 2018. Relational Summarization for Corpus Analysis. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1760–1769, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Relational Summarization for Corpus Analysis (Handler & O’Connor, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/N18-1159.pdf
- Data
- Sentence Compression