# BanglaSummEval: Reference-Free Factual Consistency Evaluation for Bangla Summarization

arXiv: https://arxiv.org/abs/2602.16843 · HTML: https://mlsanity.com/paper/2602.16843/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization](https://mlsanity.com/paper/2111.09525/) (2021)
- [Asking and Answering Questions to Evaluate the Factual Consistency of Summaries](https://mlsanity.com/paper/2004.04228/) (2020)
- [QuestEval: Summarization Asks for Fact-based Evaluation](https://mlsanity.com/paper/2103.12693/) (2021)
- [QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization](https://mlsanity.com/paper/2112.08542/) (2021)
- [Evaluating the Factual Consistency of Large Language Models Through News Summarization](https://mlsanity.com/paper/2211.08412/) (2022)
- [Unsupervised Abstractive Summarization of Bengali Text Documents](https://mlsanity.com/paper/2102.04490/) (2021)
- [Using Similarity to Evaluate Factual Consistency in Summaries](https://mlsanity.com/paper/2409.15090/) (2024)

## Built on by (most cited first, 2 of 2)

- [BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali](https://mlsanity.com/paper/2605.31483/) (2026)
- [Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali](https://mlsanity.com/paper/2610.19003/) (2026)

## Abstract (arXiv)

Evaluating factual consistency is essential for reliable text summarization, particularly in high-stakes domains such as healthcare and news. However, most existing evaluation metrics overlook Bangla, a widely spoken yet under-resourced language, and often depend on reference summaries. We introduce BanglaSummEval, a reference-free, question-answering-based framework for evaluating factual consistency in Bangla summarization. The proposed method assesses both factual accuracy and content coverage through automatically generated questions and answers derived from the source document and the summary. A single multilingual instruction-tuned language model handles question generation, question answering, candidate answer extraction, and question importance weighting. This unified design reduces system complexity and computational cost. To capture semantic consistency beyond surface-level overlap, we use BERTScore-Recall for answer comparison. We validate BanglaSummEval on 300 human-written summaries from educational and medical domains, demonstrating strong correlation with expert human judgments (Pearson's r = 0.694, Spearman's ρ = 0.763). By providing interpretable, step-wise diagnostics alongside reliable evaluation scores, BanglaSummEval offers a practical and transparent solution for factual consistency evaluation in low-resource language settings.