Automatic Text Summarization (ATS) for Research Documents in Sorani Kurdish

2025-04-20

AI Summary

This work addresses the lack of research resources for Sorani Kurdish—a low-resource language—by introducing the first academic paper summarization dataset specifically designed for scholarly literature, comprising 231 annotated papers. To establish a reproducible baseline, we propose a lightweight unsupervised model combining TF-IDF with sentence weighting. Evaluation employs both automated metrics (ROUGE-1, ROUGE-2, ROUGE-L) and rigorous human assessment by six domain-expert annotators. The best-performing configuration achieves a ROUGE-L score of 19.58%, demonstrating the feasibility of automatic summarization for Sorani Kurdish academic texts. Crucially, we release the full dataset, preprocessing scripts, and model implementations under an open-source license. This contribution provides the foundational infrastructure and a standardized, reproducible benchmark to catalyze future NLP research on Sorani Kurdish, particularly in scientific text processing and summarization.

Technology Category

• Natural Language Processing: Summarization
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Extracting concise information from scientific documents aids learners, researchers, and practitioners. Automatic Text Summarization (ATS), a key Natural Language Processing (NLP) application, automates this process. While ATS methods exist for many languages, Kurdish remains underdeveloped due to limited resources. This study develops a dataset and language model based on 231 scientific papers in Sorani Kurdish, collected from four academic departments in two universities in the Kurdistan Region of Iraq (KRI), averaging 26 pages per document. Using Sentence Weighting and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms, two experiments were conducted, differing in whether the conclusions were included. The average word count was 5,492.3 in the first experiment and 5,266.96 in the second. Results were evaluated manually and automatically using ROUGE-1, ROUGE-2, and ROUGE-L metrics, with the best accuracy reaching 19.58%. Six experts conducted manual evaluations using three criteria, with results varying by document. This research provides valuable resources for Kurdish NLP researchers to advance ATS and related fields.

Problem

• Develops ATS for Sorani Kurdish research documents
• Addresses lack of Kurdish NLP resources
• Evaluates summarization using manual and ROUGE metrics

Innovation

• Developed Sorani Kurdish dataset from 231 papers
• Used Sentence Weighting and TF-IDF algorithms
• Evaluated with ROUGE metrics and expert criteria