Summary of Automatic Summarization Of Long Documents, by Naman Chhibbar et al.
Automatic Summarization of Long Documents
by Naman Chhibbar, Jugal Kalita
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study addresses the challenge of automatically summarizing large volumes of textual data by introducing three novel algorithms that enable any Large Language Model (LLM) to efficiently process texts longer than its context size. The proposed methods allow LLMs to fully utilize their potential without requiring architectural modifications, which is particularly important for transformer-based models exceling in summarization. The experiments conducted on texts with more than 70,000 words demonstrate a significant increase in BERTScore and competitive ROUGE scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve the problem of too much text online by creating new ways to help large language models process long texts without changing their structure. These models are really good at summarizing text, but they can only handle so much information at once. The researchers developed three new methods that let these models do even more with less change. They tested these methods on very long texts and showed big improvements in how well they summarized the information. |
Keywords
» Artificial intelligence » Large language model » Rouge » Summarization » Transformer
