Algorithm parallelism for improved extractive summarization
Date
2023-08-22
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Abstract
While much work on abstractive summarization has been conducted in recent years, including state-of-the-art summarizations from GPT-4, extractive summarization's lossless nature continues to provide advantages, preserving the style and often key phrases of the original text as meant by the author. Libraries for extractive summarization abound, with a wide range of efficacy. Some do not perform much better or perform even worse than random sampling of sentences extracted from the original text. This study breathes new life to using classical algorithms by proposing parallelism through an implementation of a second order meta-algorithm in the form of the Tessellation and Recombination with Expert Decisioner (T&R) pattern, taking advantage of the abundance of already-existing algorithms and dissociating their individual performance from the implementer's biases. Resulting summaries obtained using T&R are better than any of the component algorithms.
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Subject
natural language processing
extractive summarization
meta-algorithmics
machine learning
document summarization
tessellation and recombination