TESA: A Task in Entity Semantic Aggregation for Abstractive Summarization
| Autor: | Clément Jumel, Annie Louis, Jackie Chi Kit Cheung |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Context (language use) 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Automatic summarization Task (project management) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Language model Tuple Baseline (configuration management) business computer Natural language processing 0105 earth and related environmental sciences Abstraction (linguistics) |
| Zdroj: | EMNLP (1) |
| DOI: | 10.18653/v1/2020.emnlp-main.646 |
| Popis: | Human-written texts contain frequent generalizations and semantic aggregation of content. In a document, they may refer to a pair of named entities such as 'London' and 'Paris' with different expressions: "the major cities'', "the capital cities'' and "two European cities''. Yet generation, especially, abstractive summarization systems have so far focused heavily on paraphrasing and simplifying the source content, to the exclusion of such semantic abstraction capabilities. In this paper, we present a new dataset and task aimed at the semantic aggregation of entities. TESA contains a dataset of 5.3K crowd-sourced entity aggregations of Person, Organization, and Location named entities. The aggregations are document-appropriate, meaning that they are produced by annotators to match the situational context of a given news article from the New York Times. We then build baseline models for generating aggregations given a tuple of entities and document context. We finetune on TESA an encoder-decoder language model and compare it with simpler classification methods based on linguistically informed features. Our quantitative and qualitative evaluations show reasonable performance in making a choice from a given list of expressions, but free-form expressions are understandably harder to generate and evaluate. |
| Databáze: | OpenAIRE |
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