Abstract
The increasing amount of biomedical information that is available for researchers and clinicians makes it harder to quickly find the right information. Automatic summarization of multiple texts can provide summaries specific to the user’s information needs. In this paper we look into the use named-entity recognition for graph-based summarization. We extend the LexRank algorithm with information about named entities and present EntityRank, a multi-document graph-based summarization algorithm that is solely based on named entities. We evaluate our system on a datasets of 1009 human written summaries provided by BioASQ and on 1974 gene summaries, fetched from the Entrez Gene database. The results show that the addition of named-entity information increases the performance of graph-based summarizers and that the EntityRank significantly outperforms the other methods with regard to the ROUGE measures.- Anthology ID:
- W16-5105
- Volume:
- Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Sophia Ananiadou, Riza Batista-Navarro, Kevin Bretonnel Cohen, Dina Demner-Fushman, Paul Thompson
- Venue:
- WS
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 40–49
- Language:
- URL:
- https://aclanthology.org/W16-5105
- DOI:
- Cite (ACL):
- Frederik Schulze and Mariana Neves. 2016. Entity-Supported Summarization of Biomedical Abstracts. In Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016), pages 40–49, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Entity-Supported Summarization of Biomedical Abstracts (Schulze & Neves, 2016)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W16-5105.pdf
- Data
- BioASQ