Explainable Text Summarization
Explainable Text Summarization (XAI-augmented Abstractive and Extractive Summarization) · Also known as: XAI text summarization, interpretable summarization, transparent summarization, faithfulness-aware summarization
Explainable Text Summarization augments automatic summarization models — extractive or abstractive — with post-hoc or built-in explanation methods that reveal which source sentences, tokens, or attention patterns drove each output sentence. The goal is to audit faithfulness, detect hallucinations, and build trust in model outputs in high-stakes settings such as medical or legal document review.
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When to use it
Use explainable text summarization whenever a summarization pipeline operates in a domain where errors carry real consequences — clinical notes, legal briefs, scientific abstracts, financial reports — and where stakeholders need to verify that model outputs are factually grounded. It is also appropriate when regulatory or institutional requirements mandate model transparency. Do not use it as a substitute for evaluating whether the base summarizer is strong enough: if ROUGE or BERTScore performance is poor, explanation tools will not rescue the pipeline. Avoid it when the domain is low-stakes and throughput is the primary concern, since XAI adds latency and engineering overhead.
Strengths & limitations
- Enables auditing of hallucinations and unsupported claims in abstractive summaries.
- Increases stakeholder trust in high-stakes domains by showing source attribution for each claim.
- Compatible with any pre-trained summarization model via post-hoc methods such as LIME or SHAP.
- Supports human-in-the-loop workflows where analysts validate flagged faithfulness issues.
- Facilitates regulatory compliance and documentation of model decision rationale.
- Can surface systematic biases — e.g., consistent omission of certain source sections.
- Attention weights alone are not reliable explanations; gradient or perturbation methods are needed for rigorous attribution.
- Post-hoc methods such as LIME are computationally expensive and may produce inconsistent explanations across runs.
- Faithfulness metrics (FactCC, QAEval) are themselves imperfect and can miss subtle factual errors.
- Explanation quality depends heavily on the quality of the base summarizer; a weak model cannot be made trustworthy purely through XAI.
- Human evaluation of explanations is time-consuming and does not scale to large corpora.
Frequently asked
Are attention weights enough to explain why a model generated a summary sentence?
No. Attention weights reflect where the model looked but not what caused the output. Research by Jain & Wallace (2019) and Wiegreffe & Pinter (2019) showed attention is often a poor proxy for influence. Gradient-based methods or perturbation approaches (LIME, SHAP) provide more reliable attributions.
Which XAI method works best for abstractive summarization?
There is no single best method. Integrated gradients and input-reduction approaches tend to produce more faithful attributions for transformer seq2seq models, while LIME is model-agnostic and broadly applicable. Combining attention with a dedicated faithfulness classifier (e.g., FactCC) gives complementary signal.
How do I measure faithfulness automatically?
Common options include FactCC (entailment-based), QAEval (question-answering consistency), BARTScore (generation probability), and SummaC. No metric is perfect; running two complementary measures and validating a sample manually is recommended.
Can explainable summarization be used with extractive models?
Yes, and it is simpler: each output sentence is itself a source sentence, so attribution is trivially interpretable. The faithfulness challenge is more acute for abstractive models that generate novel text.
Does adding XAI hurt summarization quality?
Post-hoc XAI methods do not modify the model and therefore do not change summary quality, only the analysis overhead. Built-in faithfulness constraints (e.g., coverage mechanisms, unlikelihood training) can slightly trade fluency for accuracy, but the effect is usually small.
Sources
- Atanasova, P., Simonsen, J. G., Lioma, C., & Augenstein, I. (2020). A diagnostic study of explainability techniques for text classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3256–3274. Association for Computational Linguistics. link ↗
- Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 1906–1919. link ↗
How to cite this page
ScholarGate. (2026, June 3). Explainable Text Summarization (XAI-augmented Abstractive and Extractive Summarization). ScholarGate. https://scholargate.app/en/deep-learning/explainable-text-summarization
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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