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There are many discussions held during political meetings, and a large numberof utterances for various topics is included in their transcripts. We need toread all of them if we want to follow speakers\' intentions or opinions about agiven topic. To avoid such a costly and time-consuming process to grasp oftenlongish discussions, NLP researchers work on generating concise summaries ofutterances. Summarization subtask in QA Lab-PoliInfo-2 task of the NTCIR-15addresses this problem for Japanese utterances in assembly minutes, and ourteam (SKRA) participated in this subtask. As a first step for summarizingutterances, we created a new pre-trained sentence embedding model, i.e. theJapanese Political Sentence-BERT. With this model, we summarize utteranceswithout labelled data. This paper describes our approach to solving the taskand discusses its results.

Summarizing Utterances from Japanese Assembly Minutes using Political Sentence-BERT-based Method for QA Lab-PoliInfo-2 Task of NTCIR-15