Summary of Structsum Generation For Faster Text Comprehension, by Parag Jain et al.
Structsum Generation for Faster Text Comprehension
by Parag Jain, Andreea Marzoca, Francesco Piccinno
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models have shown impressive capabilities on various tasks, but one area where they struggle is generating structured representations of text. We focus on tables and mind maps as two modalities for organizing data. Tables provide a more organized approach, while mind maps offer a visually dynamic and flexible way to represent sparse content. To address this limitation, we introduce effective prompting strategies for both table and mind map generation, leading to an absolute improvement in accuracy of +37pp (79%) for mind maps and +15pp (78%) for tables. Additionally, we propose Auto-QA as a means to evaluate the semantic coverage of generated structured representations using the SQuAD dataset. Our findings demonstrate the usefulness of structured representations via a text comprehension user study, with significant reductions in comprehension time compared to text when using tables (42.9%) and mind maps (31.9%). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to organize information into neat tables or colorful mind maps! This is what we’re trying to achieve with our research on large language models. These models are really good at doing lots of things, but they struggle when it comes to creating structured representations of text. We think this is important because sometimes you need to quickly understand and make sense of a lot of information. To solve this problem, we came up with some clever ways to “prompt” the models to generate better tables and mind maps. Our results show that these new strategies really work well! In fact, we were able to improve the accuracy of our generated structures by a significant amount. We also tested how people use these structured representations and found that they can help reduce the time it takes to understand information. |
Keywords
» Artificial intelligence » Prompt » Prompting
