Presentations by the Humans and For the Humans: Harnessing LLM s for Generating Persona-Aware Slides from Documents (2024)

Abstract: Scientific papers and slides are two different representations of the same underlying information, but both require substantial work to prepare. While there had been prior efforts on automating document-to-slides generation, there is still a pressing need of customizing the presentation of content aligning with the persona of target audience or duration of presentation. This paper first introduces the concept of end-user specification-aware document to slides conversion that incorporates end-user specifications into the conversion process. For this, we initially introduce a new dataset reuse the existing SciDuet dataset consisting of pairs of papers and corresponding slides decks from recent years’ *ACL conferences to create four persona-aware configurations. Secondly, we present Persona-Aware-D2S, a novel approach by finetuning LLMs using target audience feedback to create persona-aware slides from scientific documents. Our evaluation on both automated metrics and qualitative human evaluation suggests that by incorporating end-user specifications into the conversion process, our model can create presentations that are not only informative but also tailored to expectations and cognitive abilities of target audience.

## persona-aware-d2s-dataset creation prior research has predominantly addressed preparing technical conference slides (section 7), neglecting diverse presentation types, audiences, and durations. to fill this gap, we curate a novel benchmark evaluation dataset that encompasses a wider spectrum of presentation needs. our dataset focuses only on a subset of 75 papers from sciduet @xcite dataset to create persona-aware configuration slides of each paper. ## persona-aware d2s model pipeline a document d is organized into sections se and a set of multimodal content figures/tables f . each figure f @xmath0, cap q } contains an image i q and a caption cap q . we denote document content using c, heading as h and abstract of paper as a. our model pipeline takes document content c, audience background b (b p {e, ne} where experts are denoted by e and non-experts by ne) and duration of presentation l (l p {l,s} where l and s stand for long and short presentations) as input and generates final slide deck o, without including any external content. we denote input tuples @xmath1, b, l} and output slide deck as o, where probability of generating slide deck ppo | c, b, lq has to be maximized. our model pipeline is decomposed into following steps: ## experimental details our persona-aware-d2s pipeline is based on auto-regressive generative large language models (llms). we have experimented with gpt-2 (textdavinci-002), gpt-3 (text-davinci-003) and chat-gpt (gpt-3.5-turbo) as llms. in our pipeline, we have personalized both topic generation and content extraction steps and compared with nonpersonalized configurations. the trade-off between using entire paper as candidates in 3.2 (higher number of gpt calls) vs the performance of recall in candidate filtering. this step was mostly done to chunk the input prompt (for gpt3.5) to 4096 token limit, but we infer that making smaller number of gpt calls upto five might hurt the performance of candidate retrieval. our proposed models outperform the baselines for module-wise and end-to-end evaluation. we have compared our approaches using automatic rouge based evaluation for the topic generation module, and the results are tabulated in besides, when we use chunked candidate set of relevant sentences and pass it to ce module, our maximum recall stands (token limit of the candidates is 2500) at 78.89%. even after that, average f1-scores significantly improve by 12% after finetuning gpt-3.5-turbo over baselines ( ## conclusion and future work we introduce the concept of end-user specificationaware document to slides conversion. our novel three-step approach models human preferences in document to slide generation using human-in-theloop. moreover, in future, we want to let the humans exploit their creativity on top of the initial draft of persona-aware slides prepared by our models, through human-ai collaboration @xcite in which one could quickly create a slide deck improving the content and layout onthe-fly, generating or editing multimodal content through human textual feedback.