Leveraging Contextual Information for Effective Entity Salience Detection
This work addresses entity salience detection for applications like search and summarization, but it is incremental as it builds on existing language model methods.
The paper tackled the problem of identifying salient entities in text documents, showing that fine-tuning medium-sized language models with a cross-encoder architecture yields substantial performance gains over prior feature engineering approaches, as demonstrated through benchmarking on four datasets.
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.