Zahra Ashktorab, Djallel Bouneffouf, et al.
IJCAI 2025
This paper discuss topic distillation, an information retrieval problem that is emerging as a critical task for the www. Algorithms for this problem must distill a small number of high-quality documents addressing a broad topic from a large set of candidates. We give a review of the literature, and compare the problem with related tasks such as classification, clustering, and indexing. We then describe a general approach to topic distillation with applications to searching and partitioning, based on the algebraic properties of matrices derived from particular documents within the corpus. Our method - which we call special filtering - combines the use of terms, hyperlinks and anchor-text to improve retrieval performance. We give results for broad-topic queries on the www, and also give some anecdotal results applying the same techniques to US Supreme Court law cases, US patents, and a set of Wall Street Journal newspaper articles.
Zahra Ashktorab, Djallel Bouneffouf, et al.
IJCAI 2025
Yale Song, Zhen Wen, et al.
IJCAI 2013
Susan L. Spraragen
International Conference on Design and Emotion 2010
C.A. Micchelli, W.L. Miranker
Journal of the ACM