| Autor: |
Eric Nyberg, Madhura Das, Harini Kesavamoorthy, Pramati Kalwad, Khyathi Raghavi Chandu, Ashwin Naresh Kumar, Teruko Mitamura |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
Proceedings of the 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering. |
| DOI: |
10.18653/v1/w18-5310 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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