Keyphrase Based Arabic Summarizer (KPAS)

TL;DR

This paper presents an efficient extractive summarization algorithm for Arabic texts that uses keyphrases and a scoring scheme to produce summaries with good coverage, richness, and minimal redundancy, evaluated against established systems.

Contribution

The paper introduces a novel keyphrase-based extractive summarization method for Arabic that combines statistical and linguistic features for improved performance.

Findings

Effective in balancing coverage and redundancy

Achieves comparable or better results than existing systems

Demonstrates versatility across different summarization goals

Abstract

This paper describes a computationally inexpensive and efficient generic summarization algorithm for Arabic texts. The algorithm belongs to extractive summarization family, which reduces the problem into representative sentences identification and extraction sub-problems. Important keyphrases of the document to be summarized are identified employing combinations of statistical and linguistic features. The sentence extraction algorithm exploits keyphrases as the primary attributes to rank a sentence. The present experimental work, demonstrates different techniques for achieving various summarization goals including: informative richness, coverage of both main and auxiliary topics, and keeping redundancy to a minimum. A scoring scheme is then adopted that balances between these summarization goals. To evaluate the resulted Arabic summaries with well-established systems, aligned English/Arabic texts are used through the experiments.