Summarizing online discussions by filtering posts

Authors:
Mohamed Altantawy;Ahmed Rafea;Sherif Aly
Affiliations:
The American University in Cairo;The American University in Cairo;The American University in Cairo
Venue:
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Year:
2009

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Abstract

In this paper, we attempt to summarize online discussions by filtering posts. Selecting the highly related posts from the discussion boards leads to a summarized version of the discussion. Online Discussion Summarizer (ODS) is based on unsupervised information retrieval techniques. Four features are used in the summarization function; which are the term frequency inverse post frequency, title term frequency, description term frequency and author reputation. This paper shows that combining the four features in the same function results in higher accuracy than using each alone. ODS was able to summarize online discussions with an accuracy of 72%, precession of 83% and recall of 62%.