# Be Concise and Precise: Synthesizing Open-Domain Entity Descriptions from Facts

## TL;DR

This paper introduces a new model for automatically generating concise, accurate descriptions of entities from knowledge graphs, improving the informativeness of entity summaries for various NLP tasks.

## Contribution

The paper presents a novel fact-to-sequence encoder-decoder model with a copy mechanism for synthesizing entity descriptions from factual data, outperforming existing methods.

## Key findings

- Significant performance improvement over state-of-the-art models
- Effective generation of succinct and precise entity descriptions
- Enhanced utility for entity disambiguation and query answering

## Abstract

Despite being vast repositories of factual information, cross-domain knowledge graphs, such as Wikidata and the Google Knowledge Graph, only sparsely provide short synoptic descriptions for entities. Such descriptions that briefly identify the most discernible features of an entity provide readers with a near-instantaneous understanding of what kind of entity they are being presented. They can also aid in tasks such as named entity disambiguation, ontological type determination, and answering entity queries. Given the rapidly increasing numbers of entities in knowledge graphs, a fully automated synthesis of succinct textual descriptions from underlying factual information is essential. To this end, we propose a novel fact-to-sequence encoder-decoder model with a suitable copy mechanism to generate concise and precise textual descriptions of entities. In an in-depth evaluation, we demonstrate that our method significantly outperforms state-of-the-art alternatives.