  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.
