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.

自动从长文档的文本中生成演示文稿是一个具有挑战性和有用的问题。我们提出了一种基于图神经网络和语言模型组合的方法，通过学习输入文档的图形并为每张幻灯片分配内容，生成具备更好的非线性叙述性的演示文稿，并与直接使用语言模型的方法进行了实验证明我们方法的优点。