  This paper introduces the proposed automatic minuting system of the Hitachi
team for the First Shared Task on Automatic Minuting (AutoMin-2021). We utilize
a reference-free approach (i.e., without using training minutes) for automatic
minuting (Task A), which first splits a transcript into blocks on the basis of
topics and subsequently summarizes those blocks with a pre-trained BART model
fine-tuned on a summarization corpus of chat dialogue. In addition, we apply a
technique of argument mining to the generated minutes, reorganizing them in a
well-structured and coherent way. We utilize multiple relevance scores to
determine whether or not a minute is derived from the same meeting when either
a transcript or another minute is given (Task B and C). On top of those scores,
we train a conventional machine learning model to bind them and to make final
decisions. Consequently, our approach for Task A achieve the best adequacy
score among all submissions and close performance to the best system in terms
of grammatical correctness and fluency. For Task B and C, the proposed model
successfully outperformed a majority vote baseline.

Computation and Language;Artificial Intelligence;Information Retrieval;Machine Learning