Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization (2109.04994v1)

Abstract: Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In such a scenario, the topic of a conversation can vary upon progression and the key information for a certain topic is often scattered across multiple utterances of different speakers, which poses challenges to abstractly summarize dialogues. To capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives, which are expected to implicitly model the topic change and handle information scattering challenges for the dialogue summarization task. The proposed contrastive objectives are framed as auxiliary tasks for the primary dialogue summarization task, united via an alternative parameter updating strategy. Extensive experiments on benchmark datasets demonstrate that the proposed simple method significantly outperforms strong baselines and achieves new state-of-the-art performance. The code and trained models are publicly available via \href{https://github.com/Junpliu/ConDigSum}{https://github.com/Junpliu/ConDigSum}.

Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization

This paper addresses the challenges of abstractive dialogue summarization, particularly focusing on capturing topic shifts and dealing with scattered information across different dialogue utterances. Unlike structured texts, dialogues involve multiple speakers, leading to varying topics and dispersed key information. The authors propose a novel approach that leverages topic-aware contrastive learning to enhance the ability of models in summarizing dialogues abstrusely. The methodology encompasses two primary contrastive learning objectives: coherence detection and sub-summary generation, integrated as auxiliary tasks to support the main summarization task.

Methodology

The authors present an innovative use of contrastive learning frameworks designed specifically for the abstractive dialogue summarization task. The first component, coherence detection, focuses on assessing the coherence of dialogue snippets. The underlying hypothesis is that higher intra-topic coherence indicates pertinent information for summarization. This objective is self-supervised, relying on snippet sequences and their shuffled counterparts to create positive-negative pairs.

The second objective, sub-summary generation, assumes that summaries of dialogues may consist of sub-summaries that correspond to distinct topics within the dialogue. This objective aids in emphasizing the most significant information from various sections of the dialogue, thereby fostering more relevant summaries. The integration of these objectives is achieved through an alternating parameter update strategy, which manages the complex interaction between main and auxiliary tasks.

Experimental Evaluation

The authors conduct extensive experiments using benchmark datasets, including SAMSum and MediaSum. Their proposed model, termed ConDigSum, demonstrates superior performance over existing strong baselines, achieving state-of-the-art results as measured by ROUGE and BERTScore metrics. The paper confirms that both the coherence detection and sub-summary generation objectives substantially enhance the quality of generated summaries, effectively handling the intricacies of multi-speaker dialogues.

The ablation studies underscore the importance of each component. The removal of either contrastive objective leads to measurable drops in performance, with sub-summary generation contributing more significantly to the primary task compared to coherence detection. The alternating update strategy outperforms a summation objective approach, indicating the merit of dynamically incorporating these auxiliary tasks.

Implications and Future Directions

The proposed approach effectively demonstrates that contrastive learning can be harnessed to tackle complex generative tasks like dialogue summarization. Practically, this work provides robust models capable of generating more coherent and concise summaries, which are particularly valuable in applications such as customer service and meeting summarization.

Theoretically, this research extends the understanding of topic modeling within sequence-to-sequence frameworks. Future developments could explore leveraging structured representations or combining contrastive learning with other machine learning paradigms like reinforcement learning to further improve dialogue understanding and summarization. Additionally, exploring more sophisticated methods for constructing positive and negative pairs could refine the model's ability to capture nuanced dialogue dynamics.

In conclusion, this paper offers a promising direction for dialogue summarization tasks, paving the way for integrating contrastive learning into abstractive approaches that necessitate sophisticated handling of topic shifts and dispersed information.