What does this research mean for the field?

A question answering and alignment system utilizing heuristic rules for sentence matching, similarity measures for question retrieval, and a Text-to-Text Transfer Transformer (T5) for summarization can be constructed to process political meeting minutes. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The objective was to develop a system for question answering and answer alignment.

April 1, 2026Open Access

ditlab at the NTCIR-16 QA Lab-PoliInfo-3

Key Points

The objective was to develop a system for question answering and answer alignment.
Developed a QA alignment system using heuristic rules for answer matching.
Optimized heuristic rules to improve processing time.
Prepared four types of features for effective matching.
Built a QA system utilizing a similarity measure for question matching.
Employed T5 to summarize the associated answers.
Heuristic rule optimization enabled fast associations of questions to answers.
Similarity measures effectively identified original questions from summaries.
The T5 model produced concise summaries of the answers.

Abstract

The ditlab team participated in the QA alignment and Question Answering task of the NTCIR-16 QA Lab-PoliInfo-3 task. First, we developed a QA alignment system that associates each question to its answer by using heuristic rules to make paragraphs composed of the related sentences and by matching them. Heuristic rules were optimized for minutes. We prepared four types of features for matching. Second, we built a QA system that uses a similarity measure to find the original question similar to the question summary. The QA system then identified the answers associated with the original question using the results of the QA alignment described above. A Text-to-Text Transfer Transformer (T5) was used to summarize the associated answer.

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