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SoRTESum: A Social Context Framework for Single-Document Summarization Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Leveraging Semantic Annotations to Link Wikipedia and News Archives

Authors : Arunav Mishra, Klaus Berberich

Published in: Advances in Information Retrieval

Publisher: Springer International Publishing

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Abstract

The incomprehensible amount of information available online has made it difficult to retrospect on past events. We propose a novel linking problem to connect excerpts from Wikipedia summarizing events to online news articles elaborating on them. To address this linking problem, we cast it into an information retrieval task by treating a given excerpt as a user query with the goal to retrieve a ranked list of relevant news articles. We find that Wikipedia excerpts often come with additional semantics, in their textual descriptions, representing the time, geolocations, and named entities involved in the event. Our retrieval model leverages text and semantic annotations as different dimensions of an event by estimating independent query models to rank documents. In our experiments on two datasets, we compare methods that consider different combinations of dimensions and find that the approach that leverages all dimensions suits our problem best.

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previous chapter A Graph-Based Approach to Topic Clustering for Online Comments to News
next chapter Deep Learning over Multi-field Categorical Data
Footnotes
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Metadata
Title
Leveraging Semantic Annotations to Link Wikipedia and News Archives
Authors
Arunav Mishra
Klaus Berberich
Copyright Year
2016
Publisher
Springer International Publishing
Book
Advances in Information Retrieval

Print ISBN: 978-3-319-30670-4

Electronic ISBN: 978-3-319-30671-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-30671-1_3