Automatic slides generation in the absence of training data

Disseminating the main research findings is one of the main requirements to become a successful researcher. Presentation slides are the most common way to present paper content. To support researchers in slide preparation, the NLP research community has explored the use of summarization techniques to automatically generate a draft of the slides consisting of the most salient sentences or phrases. State-of-the-art methods adopt a supervised approach, which first estimates global content relevance using a set of training papers and slides, then performs content selection by optimizing also section-level coverage. How- ever, in several domains and contexts there is a lack of training data, which hinders the use of supervised models. This paper focuses on addressing the above issue by applying unsupervised summarization methods. They are exploited to generate sentence-level summaries of the paper sections, which are then refined by applying an optimization step. Furthermore, it evaluates the quality of the output slides by taking into account the original paper structure as well. The results, achieved on a benchmark collection of papers and slides, show that unsupervised models performed better than supervised ones on specific paper facets, whereas they were competitive in terms of overall quality score.