Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes

High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). Unlike standard text summarization, a dialogue summarization method should consider the multi-speaker scenario where the speakers have different roles, goals, and language styles. In a tete-a-tete, such as a customer-agent conversation, SuTaT aims to summarize for each speaker by modeling the customer utterances and the agent utterances separately while retaining their correlations. SuTaT consists of a conditional generative module and two unsupervised summarization modules. The conditional generative module contains two encoders and two decoders in a variational autoencoder framework where the dependencies between two latent spaces are captured. With the same encoders and decoders, two unsupervised summarization modules equipped with sentence-level self-attention mechanisms generate summaries without using any annotations. Experimental results show that SuTaT is superior on unsupervised dialogue summarization for both automatic and human evaluations, and is capable of dialogue classification and single-turn conversation generation

Abstractive spoken document summarization using hierarchical model with multi-stage attention diversity optimization

Abstractive summarization is a standard task for written documents, such as news articles. Applying summarization schemes to spoken documents is more challenging, especially in situations involving human interactions, such as meetings. Here, utterances tend not to form complete sentences and sometimes contain little information. Moreover, speech disfluencies will be present as well as recognition errors for automated systems. For current attention-based sequence-to-sequence summarization systems, these additional challenges can yield a poor attention distribution over the spoken document words and utterances, impacting performance. In this work, we propose a multi-stage method based on a hierarchical encoder-decoder model to explicitly model utterance-level attention distribution at training time; and enforce diversity at inference time using a unigram diversity term. Furthermore, multitask learning tasks including dialogue act classification and extractive summarization are incorporated. The performance of the system is evaluated on the AMI meeting corpus. The inclusion of both training and inference diversity terms improves performance, outperforming current state-of-the-art systems in terms of ROUGE scores. Additionally, the impact of ASR errors, as well as performance on the multitask learning tasks, is evaluated

Evaluating Robustness of Dialogue Summarization Models in the Presence of Naturally Occurring Variations

Dialogue summarization task involves summarizing long conversations while preserving the most salient information. Real-life dialogues often involve naturally occurring variations (e.g., repetitions, hesitations) and existing dialogue summarization models suffer from performance drop on such conversations. In this study, we systematically investigate the impact of such variations on state-of-the-art dialogue summarization models using publicly available datasets. To simulate real-life variations, we introduce two types of perturbations: utterance-level perturbations that modify individual utterances with errors and language variations, and dialogue-level perturbations that add non-informative exchanges (e.g., repetitions, greetings). We conduct our analysis along three dimensions of robustness: consistency, saliency, and faithfulness, which capture different aspects of the summarization model's performance. We find that both fine-tuned and instruction-tuned models are affected by input variations, with the latter being more susceptible, particularly to dialogue-level perturbations. We also validate our findings via human evaluation. Finally, we investigate if the robustness of fine-tuned models can be improved by training them with a fraction of perturbed data and observe that this approach is insufficient to address robustness challenges with current models and thus warrants a more thorough investigation to identify better solutions. Overall, our work highlights robustness challenges in dialogue summarization and provides insights for future research