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Found 4 papers, 1 papers with code
Self-Supervised Neural Topic Modeling
Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text.
Controllable Topic-Focused Abstractive Summarization
We show that our model sets a new state of the art on the NEWTS dataset in terms of topic-focused abstractive summarization as well as a topic-prevalence score.
Neural Summarization of Electronic Health Records
Using nursing notes and discharge summaries from the MIMIC-III dataset, we studied the viability of the automatic generation of various sections of a discharge summary using four state-of-the-art neural network summarization models (BART, T5, Longformer and FLAN-T5).
NEWTS: A Corpus for News Topic-Focused Summarization
Text summarization models are approaching human levels of fidelity.
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