Distant Supervision for Extractive Question Summarization
Tatsuya Ishigaki (Tokyo Tech), Kazuya Machida (Tokyo Tech), Hayato Kobayashi, Hiroya Takamura (AIST/Tokyo Tech), Manabu Okumura (Tokyo Tech)
The 42nd European Conference on Information Retrieval (ECIR 2020), 2020/4
自然言語処理 (Natural Language Processing) 情報検索 (Information Retrieval) 機械学習 (Machine Learning)
- Questions are often lengthy and difficult to understand because they tend to contain peripheral information. Previous work relies on costly human-annotated data or question-title pairs. In this work, we propose a distant supervision framework that can train a question summarizer without annotation costs or question-title pairs, where sentences are automatically annotated by means of heuristic rules. The key idea is that a single-sentence question tends to have a summary-like property. We empirically show that our models trained on the framework perform competitively with respect to supervised models without the requirement of a costly human-annotated dataset.
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