STRUCTSUM Generation for Faster Text Comprehension

Anonymous

Abstract: We consider the task of generating structured representations of text using large language models (LLMs). We focus on tables and mind maps as representative modalities. Tables are more organized way of representing data, while mind maps provide a visually dynamic and flexible approach, particularly suitable for sparse content. Despite the effectiveness of LLMs on different tasks, we show that current models struggle with generating structured outputs. In response, we present effective prompting strategies for both of these tasks. We introduce a taxonomy of problems around factuality, global and local structure, common to both modalities and propose a set of critiques to tackle these issues resulting in an absolute improvement in accuracy of $+37$pp ($79\%$) for mind maps and $+15$pp ($78\%$) for tables. To evaluate semantic coverage of generated structured representations we propose \textsc{Auto-QA}, and we verify the adequacy of \textsc{Auto-QA} using SQuAD dataset. We further evaluate the usefulness of structured representations via a text comprehension user study. The results show a significant reduction in comprehension time compared to text when using table ($42.9\%$) and mind map ($31.9\%$), without loss in accuracy.

Paper Type: long

Research Area: NLP Applications

Contribution Types: NLP engineering experiment

Languages Studied: English