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Exploding TV Sets & Disappointing Laptops: Suggesting Interesting Content in News Archives based on Surprise Estimation


Exploding TV Sets & Disappointing Laptops: Suggesting Interesting Content in News Archives based on Surprise Estimation



Published: 2021

Buchtitel: Proceedings of the 43rd European Conferene on Information Retrieval (ECIR'21)
Verlag: Springer

Referierte Veröffentlichung

BibTeX

Kurzfassung
Many archival collections have been recently digitized and made available to a wide public. The contained documents however tend to have limited attractiveness for ordinary users, since content may appear obsolete and uninteresting. Archival document collections can become more attractive for users if suitable content can be recommended to them. The purpose of this research is to propose a new research direction of Archival Content Suggestion to discover interesting content from long-term document archives that preserve information on society history and heritage. To realize this objective, we propose two unsupervised approaches for automatically discovering interesting sentences from news article archives. Our methods detect interesting content by comparing the information written in the past with one created in the present to make use of a surprise effect. Experiments on New York Times corpus show that our approaches effectively retrieve interesting content.

Download: Media:Exploding_TV_ECIR2020.pdf



Forschungsgruppe

Web Science


Forschungsgebiet

Information Retrieval, Natürliche Sprachverarbeitung, Künstliche Intelligenz