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Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization
This paper approaches the content selection problem for clinical abstractive summarization by augmenting salient ontological terms into the summarizer, and shows that this model statistically significantly boosts state-of-the-art results in terms of ROUGE metrics.
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection
An ablation study reveals that domain tuning considerably improves the classification performance of the BERT model, and error analysis shows common misclassification errors made by the model and outlines research directions for future.
Ontology-Aware Clinical Abstractive Summarization
- Sean MacAvaney, Sajad Sotudeh, Arman Cohan, Nazli Goharian, Ish A. Talati, Ross W. Filice
- Computer ScienceSIGIR
- 14 May 2019
A sequence-to-sequence abstractive summarization model augmented with domain-specific ontological information to enhance content selection and summary generation is proposed and significantly outperforms the current state-of-the-art on this task in terms of rouge scores.
On Generating Extended Summaries of Long Documents
This paper exploits hierarchical structure of the documents and incorporates it into an extractive summarization model through a multi-task learning approach and shows that the multi-tasking approach can adjust extraction probability distribution to the favor of summary-worthy sentences across diverse sections.
TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts
- Sajad Sotudeh, Hanieh Deilamsalehi, Franck Dernoncourt, Nazli Goharian
- Computer ScienceNEWSUM
- 4 October 2021
This paper introduces TLDR9+ –a large-scale summarization dataset– containing over 9 million training instances extracted from Reddit discussion forum ([HTTP]).