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