|Temporal Summarization (TS) is a new track introduced as part of the Text REtrieval Conference (TREC) in 2013. This track aims to develop systems which can return important updates related to an event over time. In TREC 2013, the TS track specifically used disaster related events such as earthquake, hurricane, bombing, etc. This thesis mainly focuses on building an effective TS system by using a combination of Information Retrieval techniques. The developed TS system returns updates related to disaster related events in a timely manner.
By participating in TREC 2013 and with experiments conducted after TREC, we examine the effectiveness of techniques such as distributional similarity for term expansion, which can be employed in building TS systems. Also, this thesis describes the effectiveness of other techniques such as stemming, adaptive sentence selection over time and de-duplication in our system, by comparing it with other baseline systems.
The second part of the thesis examines the current methodology used for evaluating TS systems. We propose a modified evaluation method which could reduce the manual effort of assessors, and also correlates well with the official track’s evaluation. We also propose a supervised learning based evaluation method, which correlates well with the official track’s evaluation of systems and could save the assessor’s time by as much as 80%.