# User-Oriented Summaries Using a PSO Based Scoring Optimization Method

## TL;DR

This paper introduces a novel extractive summarization method that employs Particle Swarm Optimization to weight sentence features, improving summary relevance by incorporating user-labeled data.

## Contribution

It presents a new PSO-based scoring optimization technique that effectively identifies key features for user-oriented extractive summaries, enhancing accuracy over prior methods.

## Key findings

- Improved summary relevance using user-labeled data.
- Enhanced accuracy compared to existing summarization techniques.
- Effective feature weighting through PSO optimization.

## Abstract

Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In~this~ article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using \textit{Particle Swarm Optimization}. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field