Leveraging Contextual Information for Effective Entity Salience
  Detection
Authors: name: Rajarshi Bhowmik, name: Marco Ponza, name: Atharva Tendle, name: Anant Gupta, name: Rebecca Jiang, name: Xingyu Lu, name: Qian Zhao, name: Daniel Preotiuc-Pietro

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.