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

arXiv: https://arxiv.org/abs/2010.12077 · HTML: https://mlsanity.com/paper/2010.12077/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [ALIGNMEET: A Comprehensive Tool for Meeting Annotation, Alignment, and Evaluation](https://mlsanity.com/paper/2205.05433/) (2022)
- [NLP for Local Governance Meeting Records: A Focus Article on Tasks, Datasets, Metrics and Benchmark](https://mlsanity.com/paper/2602.08162/) (2026)
- [CitiLink-Summ: Summarization of Discussion Subjects in European Portuguese Municipal Meeting Minutes](https://mlsanity.com/paper/2602.16607/) (2026)

## Abstract (arXiv)

There are many discussions held during political meetings, and a large number of utterances for various topics is included in their transcripts. We need to read all of them if we want to follow speakers' intentions or opinions about a given topic. To avoid such a costly and time-consuming process to grasp often longish discussions, NLP researchers work on generating concise summaries of utterances. Summarization subtask in QA Lab-PoliInfo-2 task of the NTCIR-15 addresses this problem for Japanese utterances in assembly minutes, and our team (SKRA) participated in this subtask. As a first step for summarizing utterances, we created a new pre-trained sentence embedding model, i.e. the Japanese Political Sentence-BERT. With this model, we summarize utterances without labelled data. This paper describes our approach to solving the task and discusses its results.