Publications
カンファレンス (国際) Extractive Headline Generation Based on Learning to Rank for Community Question Answering
Tatsuru Higurashi, Hayato Kobayashi, Takeshi Masuyama, Kazuma Murao
The 27th International Conference on Computational Linguistics (COLING 2018)
2018.8.20
User-generated content such as the questions on community question answering (CQA) forums does not always come with appropriate headlines, in contrast to the news articles used in various headline generation tasks. In such cases, we cannot use paired supervised data, e.g., pairs of articles and headlines, to learn a headline generation model. To overcome this problem, we propose an extractive headline generation method based on learning to rank for CQA that extracts the most informative substring from each question as its headline. Experimental results show that our method outperforms several baselines, including a prefix-based method, which is widely used in real services.
Paper :
Extractive Headline Generation Based on Learning to Rank for Community Question Answering
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PDF : Extractive Headline Generation Based on Learning to Rank for Community Question Answering