A modified model for topic detection from a corpus and a new metric
  evaluating the understandability of topics
Tomoya Kitano Yuto Miyatake Daisuke Furihata
This paper presents a modified neural model for topic detection from a corpus
and proposes a new metric to evaluate the detected topics. The new model builds
upon the embedded topic model incorporating some modifications such as document
clustering. Numerical experiments suggest that the new model performs
favourably regardless of the document's length. The new metric, which can be
computed more efficiently than widely-used metrics such as topic coherence,
provides variable information regarding the understandability of the detected
topics.