The Perils of Naive Truncation: A Context Ablation Study for Dialogue Summarization on DialogSum
Dialogue summarization systems often employ context truncation strategies to reduce computational costs. While truncation improves efficiency, its impact on summary quality and semantic fidelity remains insufficiently explored.
This study investigates the effects of naive context truncation on dialogue summarization performance using the DialogSum benchmark dataset. Through a systematic context ablation analysis, we compare full-dialogue summarization against truncated dialogue variants and evaluate performance using ROUGE and BERTScore metrics.
Our findings demonstrate that naive truncation leads to consistent degradation in summary quality, particularly for longer conversations where critical contextual information is distributed across multiple dialogue turns. The results highlight the importance of preserving conversational context and suggest that efficiency gains from aggressive truncation may come at the expense of factual completeness and semantic accuracy.
- Evaluate the impact of dialogue context on summarization quality.
- Compare full-dialogue and truncated-dialogue inputs.
- Quantify performance degradation using ROUGE and BERTScore.
- Analyze effects across short and long conversations.
- Provide empirical evidence regarding context preservation in dialogue summarization.
A large-scale dialogue summarization dataset consisting of multi-turn conversations paired with human-written summaries.
- DistilBART
- BART
- T5
Primary lightweight model:
text sshleifer/distilbart-cnn-12-6
Model receives the entire dialogue.
Model receives only the most recent K dialogue turns:
text K ∈ {0, 1, 2, 4, 6}
Performance is compared against the full-context baseline.
- ROUGE-1
- ROUGE-2
- ROUGE-L
Measures semantic similarity between generated and reference summaries.
- Full dialogue context consistently outperforms truncated inputs.
- Performance degradation increases with aggressive truncation.
- Long conversations are significantly more affected than short conversations.
- BERTScore and ROUGE metrics both indicate loss of semantic information under naive truncation.
- Efficiency gains are relatively small compared to quality degradation.
- Python
- PyTorch
- Hugging Face Transformers
- DialogSum Dataset
- ROUGE
- BERTScore
- Google Colab
If you use this work, please cite:
Kolla, D. V. The Perils of Naive Truncation: A Context Ablation Study for Dialogue Summarization on DialogSum.
Devasish Viswanadh Kolla
B.Tech Computer Science and Engineering
SRM University AP
Research Areas:
- Natural Language Processing
- Generative AI
- Dialogue Systems
- Explainable AI
- Trustworthy AI