SEL: a Unified Algorithm for Entity Linking and Saliency Detection

The Entity Linking task consists in automatically identifying and linking the entities mentioned in a text to their URIs in a given Knowledge Base. This task is very challenging due to natural language ambiguity. However, not all the entities mentioned in a document have the same utility in understanding the topics being discussed. Thus, the related problem of identifying the most relevant entities present in a document, also known as Salient Entities, is attracting increasing interest. In this paper we propose SEL, a novel supervised two-step algorithm comprehensively addressing both entity linking and saliency detection. The first step is aimed at identifying a set of candidate entities that are likely to be mentioned in the document.The second step, besides detecting linked entities, also scores them according to their saliency. Experiments conducted on two different datasets show that the proposed algorithm outperforms state-ofthe-art competitors, and is able to detect salient entities with high accuracy. Furthermore, we employed SEL for Extractive Text Summarization. We found that entity saliency can be incorporated into text summarizers to extract salient sentences from text. The resulting summarizers outperform well-known summarization systems, proving the importance of using the Salient Entities information

keywords: Entity Linking, Salient Entities, Machine Learning, Text Summarization