Ontology-Based Retrieval & Neural Approaches for BioASQ Ideal Answer Generation

Ashwin Naresh Kumar, Harini Kesavamoorthy, Madhura Das, Pramati Kalwad, Khyathi Chandu, Teruko Mitamura, Eric Nyberg


Abstract
The ever-increasing magnitude of biomedical information sources makes it difficult and time-consuming for a human researcher to find the most relevant documents and pinpointed answers for a specific question or topic when using only a traditional search engine. Biomedical Question Answering systems automatically identify the most relevant documents and pinpointed answers, given an information need expressed as a natural language question. Generating a non-redundant, human-readable summary that satisfies the information need of a given biomedical question is the focus of the Ideal Answer Generation task, part of the BioASQ challenge. This paper presents a system for ideal answer generation (using ontology-based retrieval and a neural learning-to-rank approach, combined with extractive and abstractive summarization techniques) which achieved the highest ROUGE score of 0.659 on the BioASQ 5b batch 2 test.
Anthology ID:
W18-5310
Volume:
Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venues:
BioASQ | EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
79–89
Language:
URL:
https://www.aclweb.org/anthology/W18-5310
DOI:
10.18653/v1/W18-5310
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5310.pdf