Keyphrase Based Arabic Summarizer (KPAS)
This work addresses the need for efficient summarization tools for Arabic language processing, though it is incremental as it applies existing extractive methods to a specific domain.
The paper tackles the problem of extractive summarization for Arabic texts by developing a keyphrase-based algorithm that identifies representative sentences using statistical and linguistic features, resulting in summaries that balance informativeness, topic coverage, and redundancy reduction, with evaluation conducted using aligned English/Arabic texts.
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.