The results of the Master thesis by Tobias Schubotz are being presented at the Web Intelligence conference in Singapore in December this year. The short paper is titled 'Online Temporal Summarization of News Events' by Tobias Schubotz and Ralf Krestel. The abstract of the paper is given below.
Abstract. Nowadays, an ever increasing number of news
articles is published on a daily basis. Especially after notable
national and international events or disasters, news coverage
rises tremendously. Temporal summarization is an approach to
automatically summarize such information in a timely manner.
Summaries are created incrementally with progressing time, as
soon as new information is available. Given a user-defined query,
we designed a temporal summarizer based on probabilistic lan-
guage models and entity recognition. First, all relevant documents
and sentences are extracted from a stream of news documents
using BM25 scoring. Second, a general query language model is
created which is used to detect typical sentences respective to the
query with Kullback-Leibler divergence. Based on the retrieval
result, this query model is extended over time by terms appearing
frequently during the particular event. Our system is evaluated
with a document corpus including test data provided by the Text
Retrieval Conference (TREC).