Leveraging Contextual Information for Effective Entity Salience Detection

TL;DR

This paper demonstrates that fine-tuning medium-sized language models with a cross-encoder architecture significantly improves entity salience detection in texts, outperforming traditional feature engineering methods and highlighting the task's complexity.

Contribution

The study introduces a fine-tuning approach using medium-sized language models for entity salience detection and provides a comprehensive benchmark across multiple datasets.

Findings

Fine-tuned language models outperform feature engineering methods.

Zero-shot prompting yields inferior results.

Benchmarking across four datasets confirms the effectiveness of the proposed approach.

Abstract

In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document. These entities, often deemed as salient entities, provide useful cues of the aboutness of a document to a reader. Identifying the salience of entities was found helpful in several downstream applications such as search, ranking, and entity-centric summarization, among others. Prior work on salient entity detection mainly focused on machine learning models that require heavy feature engineering. We show that fine-tuning medium-sized language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. To this end, we conduct a comprehensive benchmarking of four publicly available datasets using models representative of the medium-sized pre-trained language model family. Additionally, we show that zero-shot prompting of instruction-tuned language models yields inferior results, indicating the task's uniqueness and complexity.