@inproceedings{naresh-kumar-etal-2018-ontology,
title = "Ontology-Based Retrieval {\&} Neural Approaches for {B}io{ASQ} Ideal Answer Generation",
author = "Naresh Kumar, Ashwin and
Kesavamoorthy, Harini and
Das, Madhura and
Kalwad, Pramati and
Chandu, Khyathi and
Mitamura, Teruko and
Nyberg, Eric",
editor = "Kakadiaris, Ioannis A. and
Paliouras, George and
Krithara, Anastasia",
booktitle = "Proceedings of the 6th {B}io{ASQ} Workshop A challenge on large-scale biomedical semantic indexing and question answering",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5310/",
doi = "10.18653/v1/W18-5310",
pages = "79--89",
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."
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%0 Conference Proceedings
%T Ontology-Based Retrieval & Neural Approaches for BioASQ Ideal Answer Generation
%A Naresh Kumar, Ashwin
%A Kesavamoorthy, Harini
%A Das, Madhura
%A Kalwad, Pramati
%A Chandu, Khyathi
%A Mitamura, Teruko
%A Nyberg, Eric
%Y Kakadiaris, Ioannis A.
%Y Paliouras, George
%Y Krithara, Anastasia
%S Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F naresh-kumar-etal-2018-ontology
%X 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.
%R 10.18653/v1/W18-5310
%U https://aclanthology.org/W18-5310/
%U https://doi.org/10.18653/v1/W18-5310
%P 79-89
Markdown (Informal)
[Ontology-Based Retrieval & Neural Approaches for BioASQ Ideal Answer Generation](https://aclanthology.org/W18-5310/) (Naresh Kumar et al., BioASQ 2018)
ACL
- Ashwin Naresh Kumar, Harini Kesavamoorthy, Madhura Das, Pramati Kalwad, Khyathi Chandu, Teruko Mitamura, and Eric Nyberg. 2018. Ontology-Based Retrieval & Neural Approaches for BioASQ Ideal Answer Generation. In Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering, pages 79–89, Brussels, Belgium. Association for Computational Linguistics.