In this paper, we propose a machine learning-based method of multi-document summarization integrating sentence extraction with bunsetsu elimination. We employ Support Vector Machines for both of the modules used. To evaluate the effect of bunsetsu elimination, we participated in the multi-document summarization task at TSC-2 by the following two approaches: (1) sentence extraction only, and (2) sentence extraction + bunsetsu elimination. The results of subjective evaluation at TSC-2 show that both approaches are superior to the Lead-based method from the viewpoint of information coverage. In addition, we made extracts from given abstracts to quantitatively examine the effectiveness of bunsetsu elimination. The experimental results showed that our bunsetsu elimination makes summaries more informative. Moreover, we found that extraction based on SVMs trained by short extracts are better than the Lead-based method, but that SVMs trained by long extracts are not.

Dividing a lecture speech into segments and providing those segments as learning objects are quite general and convenient way to construct e-learning resources. However it is difficult to assign an appropriate title to each object that reflects its content. Since there are various aspects of analyzing discourse segments, it is inevitable that researchers will face the diversity when describing the "meanings" of discourse segments. In this paper, we propose the assignment of discourse segment titles from the representation of their "meanings." In this assigning procedure, we focus on the speaker's evaluation for the event or the speech object. To verify the effectiveness of our idea, we examined identification of the segment boundaries from the titles that were described in our procedure. We confirmed that the result of the identification was more accurate than that of intuitive identification.