Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
XLibris: the active reading machine
CHI 98 Cconference Summary on Human Factors in Computing Systems
From reading to retrieval: freeform ink annotations as queries
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
Journal of the ACM (JACM)
A new approach to unsupervised text summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Automatic text summarization based on the Global Document Annotation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Automated text summarization and the SUMMARIST system
TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
Automatic summarising: The state of the art
Information Processing and Management: an International Journal
Learning from the report-writing behavior of individuals
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Incremental Personalised Summarisation with Novelty Detection
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
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For one document, current summarization systems produce a uniform version of summary for all users. Personalized summarizations are necessary in order to represent users' preferences and interests. Annotation is getting important for document sharing and collaborative filtering, which in fact record users' dynamic behaviors compared to traditional steady profiles. In this paper we introduce a new summarization system based on users' annotations. Annotations and their contexts are extracted to represent features of sentences, which are given different weights for representation of the document. Our system produces two versions of summaries for each document: generic summary without considering annotations and annotation-based summary. Since annotation is a kind of personal data, annotation-based summary is tailored to user's interests to some extent. We show by experiments that annotations can help a lot in improving summarization performance compared to no annotation consideration. At the same time, we make an extensive study on users' annotating behaviors and annotations distribution, and propose a variety of techniques to evaluate the relationships between annotations and summaries, such as how the number of annotations affects the summarizing performance. A study about collaborative filtering is also made to evaluate the summarization based on annotations of similar users.