Investigating sentence weighting components for automatic summarisation

Authors:
Shao Fen Liang;Siobhan Devlin;John Tait
Affiliations:
School of Computing and Technology, University of Sunderland, Sunderland, UK;School of Computing and Technology, University of Sunderland, Sunderland, UK;School of Computing and Technology, University of Sunderland, Sunderland, UK
Venue:
Information Processing and Management: an International Journal
Year:
2007

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Abstract

The work described here initially formed part of a triangulation exercise to establish the effectiveness of the Query Term Order algorithm. It subsequently proved to be a reliable indicator for summarising English web documents. We utilised the human summaries from the Document Understanding Conference data, and generated queries automatically for testing the QTO algorithm. Six sentence weighting schemes that made use of Query Term Frequency and QTO were constructed to produce system summaries, and this paper explains the process of combining and balancing the weighting components. The summaries produced were evaluated by the ROUGE-1 metric, and the results showed that using QTO in a weighting combination resulted in the best performance. We also found that using a combination of more weighting components always produced improved performance compared to any single weighting component.