Automatic Generation of Topic Labels
This work addresses the challenge of improving topic interpretability in information retrieval, offering a novel generative method that is incremental over prior extractive approaches.
The paper tackles the problem of automatically generating interpretable labels for topics in topic modeling, proposing a neural sequence-to-sequence approach that overcomes limitations of extractive methods by generating labels from a new synthetic dataset, achieving results comparable to human-rated labels.
Topic modelling is a popular unsupervised method for identifying the underlying themes in document collections that has many applications in information retrieval. A topic is usually represented by a list of terms ranked by their probability but, since these can be difficult to interpret, various approaches have been developed to assign descriptive labels to topics. Previous work on the automatic assignment of labels to topics has relied on a two-stage approach: (1) candidate labels are retrieved from a large pool (e.g. Wikipedia article titles); and then (2) re-ranked based on their semantic similarity to the topic terms. However, these extractive approaches can only assign candidate labels from a restricted set that may not include any suitable ones. This paper proposes using a sequence-to-sequence neural-based approach to generate labels that does not suffer from this limitation. The model is trained over a new large synthetic dataset created using distant supervision. The method is evaluated by comparing the labels it generates to ones rated by humans.