Mining Interesting Trivia for Entities from Wikipedia
Abhay Prakash

TL;DR
This paper presents a system that automatically mines and ranks interesting trivia about entities from Wikipedia pages to enhance user engagement, using a learned interestingness model trained on web data.
Contribution
It introduces a novel approach for extracting and ranking trivia from Wikipedia using a learned interestingness model based on linguistic and entity features.
Findings
System outperforms baselines in movies and celebrity domains
Rich feature set effectively surfaces interesting trivia
Qualitative analysis confirms feature importance in ranking
Abstract
Trivia is any fact about an entity, which is interesting due to any of the following characteristics - unusualness, uniqueness, unexpectedness or weirdness. Such interesting facts are provided in 'Did You Know?' section at many places. Although trivia are facts of little importance to be known, but we have presented their usage in user engagement purpose. Such fun facts generally spark intrigue and draws user to engage more with the entity, thereby promoting repeated engagement. The thesis has cited some case studies, which show the significant impact of using trivia for increasing user engagement or for wide publicity of the product/service. In this thesis, we propose a novel approach for mining entity trivia from their Wikipedia pages. Given an entity, our system extracts relevant sentences from its Wikipedia page and produces a list of sentences ranked based on their…
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Taxonomy
TopicsVideo Analysis and Summarization · Wikis in Education and Collaboration · Advanced Text Analysis Techniques
