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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Topic modeling is pivotal in discerning hidden semantic structures withintexts, thereby generating meaningful descriptive keywords. While innovativetechniques like BERTopic and Top2Vec have recently emerged in the forefront,they manifest certain limitations. Our analysis indicates that these methodsmight not prioritize the refinement of their clustering mechanism, potentiallycompromising the quality of derived topic clusters. To illustrate, Top2Vecdesignates the centroids of clustering results to represent topics, whereasBERTopic harnesses C-TF-IDF for its topic extraction.In response to thesechallenges, we introduce "TF-RDF" (Term Frequency - Relative DocumentFrequency), a distinctive approach to assess the relevance of terms within adocument. Building on the strengths of TF-RDF, we present MPTopic, a clusteringalgorithm intrinsically driven by the insights of TF-RDF. Through comprehensiveevaluation, it is evident that the topic keywords identified with the synergyof MPTopic and TF-RDF outperform those extracted by both BERTopic and Top2Vec.

MPTopic: Improving topic modeling via Masked Permuted pre-training