Active learning of extractive reference summaries for lecture speech summarization
Justin Jian Zhang, Pascale Fung · 2009
We propose using active learning for tagging extractive reference summary of lecture speech.The training process of feature-based summarization model usually requires a large amount of training data with high-quality reference summaries.Human production of such summaries is tedious, and since inter-labeler agreement is low, very unreliable.Active learning helps assuage this problem by automatically selecting a small amount of unlabeled documents for humans to hand correct.Our method chooses the unlabeled documents according to the similarity score between the document and the comparable resource-PowerPoint slides.After manual correction, the selected documents are returned to the training pool.Summarization results show an increasing learning curve of ROUGE-L F-measure, from 0.44 to 0.514, consistently higher than that of using randomly chosen training samples.