Papers by Truc Lu

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Challenge:	Abstractive summarization models have been proven effective in creating fluent and informative summaries, but they suffer from the short-range dependency problem, causing them to produce summary that miss the key points of document.
Approach:	They propose a neural topic model empowered with normalizing flow to capture global semantics of the document and integrate them into the summarization model.
Outcome:	The proposed model outperforms state-of-the-art summarization models on five common text summarizing datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed.