Controllable Neural Dialogue Summarization with Personal Named Entity Planning
Proposes a controllable neural dialogue summarization framework with personal entity planning, improving factual consistency and diversity; achieves ROUGE-2 of 58.7 on SAMSum.
Key Findings
Methodology
This paper introduces a Transformer-based conditional generation model incorporating personal named entity planning. During training, it leverages occurrence order and coreference graphs via GCNs to enhance temporal coherence and factual accuracy. The model supports two inference modes: comprehensive planning (all entities) and focus planning (single entity). The training involves extracting entity sets, computing occurrence sequences, and integrating coreference information into the encoder-decoder architecture. Data augmentation through entity exchange further boosts robustness. Evaluation on SAMSum shows significant improvements over baselines in ROUGE scores and factual correctness, validated by both automatic metrics and human judgment.
Key Results
- Using BART-Large with entity planning, ROUGE-2 scores increased from 52.9 to 58.7, with notable gains in factual consistency and fluency. The model with coreference and data augmentation outperformed baselines, demonstrating robustness and flexibility.
- Automatic factual correctness evaluation showed a 3% increase in F1 scores after data augmentation, and human assessments confirmed better factual alignment. The model effectively generates diverse summaries conditioned on different entity plans.
- The approach enables personalized summaries from different perspectives, suitable for applications like customer service, meeting summaries, and personalized assistants, with strong potential for real-world deployment.
Significance
This work addresses key challenges in dialogue summarization—balancing factual accuracy and diversity—by introducing a controllable, entity-aware framework. It advances the state-of-the-art in personalized, factually consistent dialogue summaries, with broad implications for industry applications such as customer support, virtual assistants, and automated reporting. The integration of entity planning and coreference information sets a new direction for controllable text generation, bridging the gap between generic models and user-specific needs. Its robustness and flexibility pave the way for more intelligent, personalized dialogue systems, fostering more natural and trustworthy human-AI interactions.
Technical Contribution
The paper's main technical innovations include the integration of personal named entity planning into a Transformer-based sequence-to-sequence model, the use of occurrence-based entity sequences for flexible control, and the application of graph convolutional networks to incorporate coreference information. The entity exchange data augmentation strategy further enhances model robustness. These contributions collectively enable multi-angle, factually accurate, and personalized dialogue summarization, surpassing existing methods that lack explicit controllability and entity-aware mechanisms. The framework also demonstrates how to effectively combine multiple sources of linguistic information for improved generation quality.
Novelty
This is the first work to embed personal named entity planning directly into neural dialogue summarization, allowing explicit control over the content focus. Unlike previous models that generate a single generic summary, this approach supports multiple perspectives and entity-specific summaries, addressing the limitations of existing methods in diversity and factuality. The combination of occurrence-based entity sequences, coreference graph modeling, and data augmentation introduces a novel, comprehensive solution for personalized dialogue summarization, marking a significant step forward in controllable natural language generation.
Limitations
- The model heavily relies on accurate entity recognition and coreference resolution; errors in these steps can propagate and degrade summary quality, especially in complex or noisy dialogues.
- Training complexity and computational costs are high due to multi-component architecture and graph processing, posing challenges for real-time deployment.
- Current evaluation is limited to SAMSum; generalization to other domains and longer dialogues remains to be validated, requiring further dataset expansion and robustness testing.
Future Work
Future directions include extending the framework to multi-modal data, integrating visual and acoustic cues for richer context understanding. Efforts will focus on reducing computational overhead, enabling real-time applications. Expanding datasets across diverse domains will improve generalization. Additionally, exploring more sophisticated entity planning strategies, such as hierarchical or multi-entity control, can further enhance personalization. Combining this approach with reinforcement learning could optimize controllability and factuality jointly, pushing the boundaries of personalized dialogue systems.
AI Executive Summary
This study introduces a novel controllable neural dialogue summarization framework that leverages personal named entity planning to generate diverse, factually consistent summaries. Traditional dialogue summarization models often struggle to balance informativeness, factual accuracy, and personalization. To address this, the authors propose a Transformer-based encoder-decoder architecture integrated with entity occurrence sequences and coreference graphs. During training, the model learns to generate summaries conditioned on explicit entity plans, enabling flexible control over the focus and scope of summaries.
The core technical innovation lies in the combination of occurrence-based entity sequencing and graph convolutional networks to incorporate coreference information, significantly improving temporal coherence and factual correctness. The framework supports two inference modes: comprehensive planning, which covers all entities for broad summaries, and focus planning, which targets specific entities for personalized summaries. Data augmentation through entity exchange further enhances robustness against data sparsity.
Extensive experiments on the SAMSum dataset demonstrate that the proposed model outperforms baseline methods, achieving ROUGE-2 scores of 58.7 and notable improvements in factual correctness as validated by both automatic and human evaluations. The results highlight the model’s ability to generate fluent, diverse, and accurate summaries tailored to different entity-focused requirements.
This work advances dialogue summarization by enabling explicit control over content focus, addressing key challenges in factuality and personalization. Its implications extend to real-world applications like customer service, automated reporting, and personalized virtual assistants, fostering more trustworthy and adaptable AI systems. Future research will explore multi-modal integration, efficiency improvements, and broader domain adaptation, further pushing the frontier of personalized natural language generation.
Deep Dive
Abstract
In this paper, we propose a controllable neural generation framework that can flexibly guide dialogue summarization with personal named entity planning. The conditional sequences are modulated to decide what types of information or what perspective to focus on when forming summaries to tackle the under-constrained problem in summarization tasks. This framework supports two types of use cases: (1) Comprehensive Perspective, which is a general-purpose case with no user-preference specified, considering summary points from all conversational interlocutors and all mentioned persons; (2) Focus Perspective, positioning the summary based on a user-specified personal named entity, which could be one of the interlocutors or one of the persons mentioned in the conversation. During training, we exploit occurrence planning of personal named entities and coreference information to improve temporal coherence and to minimize hallucination in neural generation. Experimental results show that our proposed framework generates fluent and factually consistent summaries under various planning controls using both objective metrics and human evaluations.