Automatic Summarization of Long Documents

Autor:	Chhibbar, Naman, Kalita, Jugal
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	A vast amount of textual data is added to the internet daily, making utilization and interpretation of such data difficult and cumbersome. As a result, automatic text summarization is crucial for extracting relevant information, saving precious reading time. Although many transformer-based models excel in summarization, they are constrained by their input size, preventing them from processing texts longer than their context size. This study introduces three novel algorithms that allow any LLM to efficiently overcome its input size limitation, effectively utilizing its full potential without any architectural modifications. We test our algorithms on texts with more than 70,000 words, and our experiments show a significant increase in BERTScore with competitive ROUGE scores. Comment: 9 pages (including bibliography) with 6 figures. ACL 2023 proceedings format
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.05903 Zobrazit plný text záznamu View this record from Arxiv