Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
TableSeer: automatic table metadata extraction and searching in digital libraries
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Blind men and elephants: What do citation summaries tell us about a research article?
Journal of the American Society for Information Science and Technology
Math information retrieval: user requirements and prototype implementation
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
Workload analysis for scientific literature digital libraries
International Journal on Digital Libraries - Special Issue on Very Large Digital Libraries
Automatic extraction of citation information in Japanese patent applications
International Journal on Digital Libraries - Special Issue on Very Large Digital Libraries
In-browser summarisation: generating elaborative summaries biased towards the reading context
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Scientific paper summarization using citation summary networks
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Topic-driven multi-document summarization with encyclopedic knowledge and spreading activation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Classification of research papers into a patent classification system using two translation models
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
Keyphrase extraction in scientific publications
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
Focused and aggregated search: a perspective from natural language generation
Information Retrieval
Automatic extraction and resolution of bibliographical references in patent documents
IRFC'10 Proceedings of the First international Information Retrieval Facility conference on Adbances in Multidisciplinary Retrieval
Improving MeSH classification of biomedical articles using citation contexts
Journal of Biomedical Informatics
Summarization of scientific documents by detecting common facts in citations
Future Generation Computer Systems
Hi-index | 0.00 |
The amount of scientific material available electronically is forever increasing. This makes reading the published literature, whether to stay up-to-date on a topic or to get up to speed on a new topic, a difficult task. Yet, this is an activity in which all researchers must be engaged on a regular basis. Based on a user requirements analysis, we developed a new research tool, called the Citation-Sensitive In-Browser Summariser (CSIBS), which supports researchers in this browsing task. CSIBS enables readers to obtain information about a citation at the point at which they encounter it. This information is aimed at enabling the reader to determine whether or not to invest the time in exploring the cited article further, thus alleviating information overload. CSIBS builds a summary of the cited document, bringing together meta-data about the document and a citation-sensitive preview that exploits the citation context to retrieve the sentences from the cited document that are relevant at this point. This paper briefly presents our user requirements analysis, then describes the system and, finally, discusses the observations from an initial pilot study. We found that CSIBS facilitates the relevancy judgment task, by increasing the users' self-reported confidence in making such judgements.