Leveraging Contextual Information for Effective Entity Salience
  Detection
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.
6507a7124a8839a8bd9686bc
Rajarshi Bhowmik
6507a7124a8839a8bd9686bd
Marco Ponza
admin_assigned
2023-09-18T10:30:52.476Z
6318f9f7f18d0b5d9998a889
/avatars/6fac81cfc779da1f92fb1924627cc214.svg
Ponza
user
mponza
6507a7124a8839a8bd9686be
Atharva Tendle
admin_assigned
2023-09-18T10:27:19.628Z
62e170976fb6e362b4a9e83c
/avatars/297b82f3c7060e2db93e1cca529d6912.svg
Atharva Tendle
user
atendle
6507a7124a8839a8bd9686bf
Anant Gupta
admin_assigned
2023-09-18T10:28:14.877Z
63efeeb05c2ceb16fc6f7d0e
/avatars/592e1a865f24f8910b63a50545effe52.svg
Anant Gupta
user
anant718
6507a7124a8839a8bd9686c0
Rebecca Jiang
6507a7124a8839a8bd9686c1
Xingyu Lu
admin_assigned
2023-09-18T10:28:51.101Z
6426bd4ca7723d62b53b9069
/avatars/e3cd3f3a0beb4ff8f1095ced783ff0e1.svg
luxingyu
user
happy905
6507a7124a8839a8bd9686c2
Qian Zhao
6507a7124a8839a8bd9686c3
Daniel Preotiuc-Pietro
extracted_confirmed
2023-09-18T16:10:10.607Z
625050f1d2d191ac4332e9c2
https://cdn-avatars.huggingface.co/v1/production/uploads/1649430742847-noauth.png
Daniel Preotiuc-Pietro
user
danielpreotiuc
Leveraging Contextual Information for Effective Entity Salience
  Detection
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.
Fine-tuning medium-sized language models with a cross-encoder style significantly improves salient entity detection compared to feature engineering approaches.
cross-encoder style zero-shot prompting instruction-tuned language models medium-sized pre-trained language models salient entity detection