Presentations are not always linear! GNN meets LLM for Text Document-to-Presentation Transformation with Attribution (2024)

Abstract: Automatically generating a presentation from the text of a long document is a challenging and useful problem. In contrast to a flat summary, a presentation needs to have a better and non-linear narrative, i.e., the content of a slide can come from different and non-contiguous parts of the given document. However, it is difficult to incorporate such non-linear mapping of content to slides and ensure that the content is faithful to the document. LLMs are prone to hallucination and their performance degrades with the length of the input document. Towards this, we propose a novel graph based solution where we learn a graph from the input document and use a combination of graph neural network and LLM to generate a presentation with attribution of content for each slide. We conduct thorough experiments to show the merit of our approach compared to directly using LLMs for this task.

## introduction presentations are a very effective medium of communication in several day-to-day and business workflows. compared to a flat summarization, generating a presentation is more complex because it should have a nice narrative and coherence in the content along with the ability to convey the core ideas to the audience. this makes generating presentation a very tedious process for humans @xcite . document-to-slide generation using automatic methods has been garnering attention for several years. these methods include using handcrafted and pre-defined heuristics or web schemas @xcite . there are approaches which generate the agenda (i.e., sequence of slide titles) of a presentation based on the sections present in the document @xcite figure 1 : a presentation (right) with non-linear narrative and attribution to the source document @xcite or require users to provide an agenda @xcite and subsequently generate the slides as a single-document query-based summarization. however, manually coming up with an agenda is a difficult task, particularly for documents in the range of 10s of pages. this issue can be mitigated to an extent given the recent advances in generative language models @xcite with increasing context limits, in which we can prompt a large language model (llm) to ingest the entire document context and generate an outline and the text content for a presentation. @xcite explored the possibility of end-user specificationaware document to slides conversion by fine-tuned llms. more recently, @xcite proposed a multi-staged zero-shot llm based approach to generate presentation slides from a document. however, these approaches have three major limitations. first, the llms tend to hallucinate more and they often ignore the middle portion of a context as the context length grows @xcite . this can be a serious issue if we want to generate slides from large documents and ensure good coverage of all the important concepts in the presentation. second, processing the entire document in its reading order by an llm often results in a summary-like overview of the document, as opposed to capturing a narrative-centric view that is required for a presentation. narratives (flow of information in the form of a story) @xcite in presentations can be non-linear in nature, i.e., paragraphs from across multiple sections of an input document contribute to a slide, and this is not necessarily in the linear reading order of the document (refer to figure 1 ). as shown in section 4.6, non-linearity in presentations generated by the authors for the set of research papers in sciduet dataset @xcite is 38.6%, whereas the ones generated by a gpt-based clustering baseline (gdp-gpt in section 4.2) has only 1.2%. to generate these narratives, the non-linear relationship between the various pieces of content in the given document needs to be captured. moreover, llms do not attribute the source content (e.g., a paragraph) for each part (e.g., a slide) of the generated content. this attribution is necessary to improve the reliability of the generated presentation and for further editing. one can potentially think of posing the problem of non-linear way of generating presentation as a classification task of classifying a sequence of text elements (say, paragraphs) to one of the k classes where each class represents a slide; or a clustering task of cluster the paragraphs to k clusters. however, the number of slides needed from a document cannot be fixed over a set of documents. it can even vary for the same document depending on the audience, intent and the duration of presentation. thus, it is not possible to pose it as a k-class classification task. also, clustering as an unsupervised task can cluster text based on multiple aspects such as frequency of common words, common sub-topics, etc., where each generated cluster does not match to a slide. to address the research gaps mentioned above, we propose a novel method of generating text presentation from a long input document as shown in figure 2 . our motivation is to infer the structure present between the text elements (i.e., paragraphs) of a document via corresponding latent slides (as shown in figure 1 ) by a learnable graph. following are the contributions made in this paper: 1. automatically generating a non-linear presentation integrated with the content attribution from a given long document is a novel task to the best of our knowledge. our method by design is able to capture the nonlinearity in generating the presentation and it attributes the source paragraphs for each generated slide within the presentation. 3. we propose an evaluation framework which includes both automated and human evaluated metrics for document to presentation transformation. our analysis shows the merit of gdp over the approaches that directly use sota llm along with intelligent prompting techniques. ## problem formulation we are given with a training dataset which is a set of documents and the corresponding presentation slides. let us denote this dataset as these paragraphs are indexed following the reading order in the document. similarly, p i is a human-generated presentation from the document d i . a presentation is a sequence of slides. so, please note that different documents can have different number of paragraphs and the corresponding presentations can have different number of slides. as discussed in section 1, we consider both the input document and the generated slides to contain only text. ideally, the presentation should cover all the important aspects of the input document, with a nice flow of information such that it is easy to follow by a broader audience. given this data, our goal is to generate a presentation for each document present in a test set ## solution approach following are the details of different components of our proposed approach gdp. figure 2 shows the overall architecture. we assume the input documents are in pdf format. we use a publicly available pdf extract api + to extract the text content from the documents. the output of extract is processed in such a way that we have the section and subsection titles and the text in the form of paragraphs within each section or subsection. the sequence of text elements is the same as the reading order of text in the pdf. we are not considering images, tables and other multimodal information present in the document in this work. ## discussions and conclusion this paper presents an end-to-end novel approach, referred as gdp, for transforming a long document into a text presentation. gdp employs a classifier to build a document graph, followed by a graph neural network and clustering. it then uses an llm to generate slides from each paragraph cluster. we propose evaluation frameworks, and the results indicate several drawbacks of directly using gpt-based approaches with different prompting techniques. the evaluation shows that gdp can automatically generate a presentation suitable as a first draft.