Automatically summarising Web sites: is there a way around it?
Proceedings of the ninth international conference on Information and knowledge management
Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Whetting the appetite of scientists: producing summaries tailored to the citation context
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Seed and Grow: augmenting statistically generated summary sentences using schematic word patterns
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
Web Semantics: Science, Services and Agents on the World Wide Web
Focused and aggregated search: a perspective from natural language generation
Information Retrieval
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
| Hi-index | 0.00 |
We investigate elaborative summarisation, where the aim is to identify supplementary information that expands upon a key fact. We envisage such summaries being useful when browsing certain kinds of (hyper-)linked document sets, such as Wikipedia articles or repositories of publications linked by citations. For these collections, an elaborative summary is intended to provide additional information on the linking anchor text. Our contribution in this paper focuses on identifying and exploring a real task in which summarisation is situated, realised as an In-Browser tool. We also introduce a neighbourhood scoring heuristic as a means of scoring matches to relevant passages of the document. In a preliminary evaluation using this method, our summarisation system scores above our baselines and achieves a recall of 57% annotated gold standard sentences.