Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose an active learning approach for feature-based extractive summarization of lecture speech. Most state-of-the-art speech summarization systems are trained by using a large amount of human reference summaries. Active learning targets to minimize human annotation efforts by automatically selecting a small amount of unlabeled examples for labeling. Our method chooses the unlabeled examples according to a combination of informativeness criterion and robustness criterion. Our summarization results show an increasing learning curve of ROUGE-L F-measure, from 0.44 to 0.54, consistently higher than that of using randomly chosen training samples.  also show that, by following the rhetorical structure in presentation slides, it is possible for humans to produce Ȝgold standardȝ reference summaries with very high inter-labeler agreement.
['Justin Jian Zhang', 'Ricky Ho Yin Chan', 'Pascale Fung']
I am writing a research paper on a idea Extractive speech summarization by active learning
[{"from": "human", "value": "I am writing a research paper on a idea Extractive speech summarization by active learning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose an active learning approach for feature-based extractive summarization of lecture speech. Most state-of-the-art speech summarization systems are trained by using a large amount of human reference summaries. Active learning targets to minimize human annotation efforts by automatically selecting a small amount of unlabeled examples for labeling. Our method chooses the unlabeled examples according to a combination of informativeness criterion and robustness criterion. Our summarization results show an increasing learning curve of ROUGE-L F-measure, from 0.44 to 0.54, consistently higher than that of using randomly chosen training samples.  also show that, by following the rhetorical structure in presentation slides, it is possible for humans to produce \u021cgold standard\u021d reference summaries with very high inter-labeler agreement."}]