English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Sequence-to-sequence neural networks have recently achieved great success inabstractive summarization, especially through fine-tuning large pre-trainedlanguage models on the downstream dataset. These models are typically decodedwith beam search to generate a unique summary. However, the search space isvery large, and with the exposure bias, such decoding is not optimal. In thispaper, we show that it is possible to directly train a second-stage modelperforming re-ranking on a set of summary candidates. Our mixture-of-expertsSummaReranker learns to select a better candidate and consistently improves theperformance of the base model. With a base PEGASUS, we push ROUGE scores by5.44% on CNN-DailyMail (47.16 ROUGE-1), 1.31% on XSum (48.12 ROUGE-1) and 9.34%on Reddit TIFU (29.83 ROUGE-1), reaching a new state-of-the-art. Our code andcheckpoints will be available at https://github.com/ntunlp/SummaReranker.

SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization