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Erschienen in:
Stay on Topic, Please: Aligning User Comments to the Content of a News Article Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Exploding TV Sets and Disappointing Laptops: Suggesting Interesting Content in News Archives Based on Surprise Estimation

verfasst von : Adam Jatowt, I-Chen Hung, Michael Färber, Ricardo Campos, Masatoshi Yoshikawa

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Mitigating the Position Bias of Transformer Models in Passage Re-ranking
Nächstes Kapitel Label Definitions Augmented Interaction Model for Legal Charge Prediction
Fußnoten
5
One could imagine a service that automatically detects interesting sentences or headlines for broad topics and publishes them daily on web portals of underlying document archives.
 
10
We have also experimented with embedding models but they did not perform better.
 
12
We set n=5 as the number of top sentences returned for every top-ranked topic in Topic-based MRRW, and for each top-ranked topic pair in Topic Pair-based MRRW method and Topic co-occurrence methods.
 
13
Anecdotally, this particular example triggered recollections of childhood memories of one author. His grandparents owned a USSR-produced TV set and often warned him not to sit close to it when he visited their home. Only now, he could understand that the fears of his relatives were actually not without a substance. On a more general note, exploring news archives offers chances for learning about history, and might sometimes even lead to serendipitous discoveries and recollections as this example demonstrates.
 
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Metadaten
Titel
Exploding TV Sets and Disappointing Laptops: Suggesting Interesting Content in News Archives Based on Surprise Estimation
verfasst von
Adam Jatowt
I-Chen Hung
Michael Färber
Ricardo Campos
Masatoshi Yoshikawa
Copyright-Jahr
2021
Buch
Advances in Information Retrieval

Print ISBN: 978-3-030-72112-1

Electronic ISBN: 978-3-030-72113-8

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-72113-8_17

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