Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Group discussions are used for various purposes, such as creating new ideas and making a group decision. It is desirable to archive the results and processes of the discussion as useful resources for the group. Therefore, a key technology would be a way to extract meaningful resources from a group discussion. To accomplish this goal,  propose classification models that select meeting extracts to be included in the discussion summary based on nonverbal behavior such as attention, head motion, prosodic features, and co-occurrence patterns of these behaviors.  create different prediction models depending on the degree of extract-worthiness, which is assessed by the agreement ratio among human judgments. Our best model achieves 0.707 in F-measure and 0.75 in recall rate, and can compress a discussion into 45% of its original duration. The proposed models reveal that nonverbal information is indispensable for selecting meeting extracts of a group discussion. One of the future directions is to implement the models as an automatic meeting summarization system.
['Fumio Nihei', 'Yukiko I. Nakano', 'Yutaka Takase']
I am writing a research paper on a idea Meeting extracts for discussion summarization based on multimodal nonverbal information
[{"from": "human", "value": "I am writing a research paper on a idea Meeting extracts for discussion summarization based on multimodal nonverbal information"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Group discussions are used for various purposes, such as creating new ideas and making a group decision. It is desirable to archive the results and processes of the discussion as useful resources for the group. Therefore, a key technology would be a way to extract meaningful resources from a group discussion. To accomplish this goal,  propose classification models that select meeting extracts to be included in the discussion summary based on nonverbal behavior such as attention, head motion, prosodic features, and co-occurrence patterns of these behaviors.  create different prediction models depending on the degree of extract-worthiness, which is assessed by the agreement ratio among human judgments. Our best model achieves 0.707 in F-measure and 0.75 in recall rate, and can compress a discussion into 45% of its original duration. The proposed models reveal that nonverbal information is indispensable for selecting meeting extracts of a group discussion. One of the future directions is to implement the models as an automatic meeting summarization system."}]