Annotation and Analysis of Extractive Summaries for the Kyutech Corpus

Abstract

Summarization of multi-party conversation is one of the important tasks in natural language processing. For conversation summarization tasks, corpora have an important role to analyze characteristics of conversations and to construct a method for summary generation. We are developing a freely available Japanese conversation corpus for a decision-making task. We call it the Kyutech corpus. The current version of the Kyutech corpus contains topic tags of each utterance and reference summaries of each conversation. In this paper, we explain an annotation task of extractive summaries. In the annotation task, we annotate an importance tag for each utterance and link utterances with sentences in reference summaries that already exist in the Kyutech corpus. By using the annotated extractive summaries, we can evaluate extractive summarization methods on the Kyutech corpus. In the experiment, we compare some methods based on machine learning techniques with some features.

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