Abstract
We describe entity summarization, the task of producing informative text summaries for an entity described across multiple documents in a collection. Existing (multi-)document summarization techniques are applied as baselines to this task, which we generalize to allow for joint information extraction and summarization. Through user evaluations across a variety of approaches, we discover what is most preferred in an entity summary. In particular we find that distantly supervised information extractors lead to significant improvements over lexical approaches, demonstrating the utility of extraction technologies for a task other than knowledge base population.
Original language | English (US) |
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Pages (from-to) | 51-58 |
Number of pages | 8 |
Journal | CEUR Workshop Proceedings |
Volume | 2127 |
State | Published - 2018 |
Event | Joint 1st International Workshop on Professional Search, the 2nd Workshop on Knowledge Graphs and Semantics for Text Retrieval, Analysis, and Understanding and the International Workshop on Data Search, ProfS-KG4IR-Data:Search 2018 - Ann Arbor, United States Duration: Jul 12 2018 → … |
ASJC Scopus subject areas
- General Computer Science