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

This paper addresses the problem of generating questions from a given contextand an answer, specifically focusing on questions that require multi-hopreasoning across an extended context. Previous studies have suggested that keyphrase selection is essential for question generation (QG), yet it is stillchallenging to connect such disjointed phrases into meaningful questions,particularly for long context. To mitigate this issue, we propose MultiFactor,a novel QG framework based on multi-level content planning. Specifically,MultiFactor includes two components: FA-model, which simultaneously selects keyphrases and generates full answers, and Q-model which takes the generated fullanswer as an additional input to generate questions. Here, full answergeneration is introduced to connect the short answer with the selected keyphrases, thus forming an answer-aware summary to facilitate QG. Both FA-modeland Q-model are formalized as simple-yet-effective Phrase-EnhancedTransformers, our joint model for phrase selection and text generation.Experimental results show that our method outperforms strong baselines on twopopular QG datasets. Our code is available athttps://github.com/zeaver/MultiFactor.

Improving Question Generation with Multi-level Content Planning