Publications
CONFERENCE (INTERNATIONAL) 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)
April 14, 2020
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.
Paper : Distant Supervision for Extractive Question Summarization (external link)
PDF : Distant Supervision for Extractive Question Summarization