DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization (2109.02492v2)

Abstract: Dialogue is an essential part of human communication and cooperation. Existing research mainly focuses on short dialogue scenarios in a one-on-one fashion. However, multi-person interactions in the real world, such as meetings or interviews, are frequently over a few thousand words. There is still a lack of corresponding research and powerful tools to understand and process such long dialogues. Therefore, in this work, we present a pre-training framework for long dialogue understanding and summarization. Considering the nature of long conversations, we propose a window-based denoising approach for generative pre-training. For a dialogue, it corrupts a window of text with dialogue-inspired noise, and guides the model to reconstruct this window based on the content of the remaining conversation. Furthermore, to process longer input, we augment the model with sparse attention which is combined with conventional attention in a hybrid manner. We conduct extensive experiments on five datasets of long dialogues, covering tasks of dialogue summarization, abstractive question answering and topic segmentation. Experimentally, we show that our pre-trained model DialogLM significantly surpasses the state-of-the-art models across datasets and tasks. Source code and all the pre-trained models are available on our GitHub repository (https://github.com/microsoft/DialogLM).

Analysis of DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization

This paper introduces DialogLM, a pre-trained model specifically designed to address the challenges posed by long dialogues, which are commonplace in real-world multi-person interactions such as meetings and interviews. Existing LLMs have predominantly been focused on short dialogues with limited interaction dynamics. DialogLM aims to fill this gap by implementing a novel pre-training framework tailored for long dialogue understanding and summarization tasks.

Methodology

The authors propose a pre-training task called window-based denoising, where a window of multiple turns within a dialogue is corrupted with various types of noise reflective of real conversational disruptions. These include Speaker Mask, Turn Splitting, Turn Merging, Text Infilling, and Turn Permutation. The model is then trained to reconstruct this window, leveraging the remaining context from the dialogue. This design incorporates the intrinsic dialogic structures and encourages the model to capture coherent and informative dialogue segments, which are critical for downstream tasks such as summarization and abstractive question answering.

DialogLM employs a hybrid attention mechanism combining sparse attention for capturing local dependencies and global attention for a comprehensive understanding of the full dialogue. This architecture enables DialogLM to handle inputs of up to 8,000 tokens, making it adaptable to extensive conversation contexts.

Experimental Evaluation

The performance of DialogLM was validated against benchmark datasets in two primary domains: meetings (AMI, ICSI, QMSum) and screenplays (ForeverDreaming, TVMegaSite). Across these datasets, DialogLM demonstrated superior performance compared to existing models including BART, Longformer, and HMNet, marking new state-of-the-art results in several cases. Specifically, it achieved notable improvements on ROUGE scores, particularly for datasets with highly verbose dialogues like ICSI and QMSum.

Additionally, DialogLM was tested on the task of dialogue segmentation, again outstripping baseline performance through lower Pk and WinDiff scores. These results underscore the model's efficacy at comprehending topic boundaries in dialogues—a crucial capability for accurate summarization.

Implications and Future Developments

DialogLM presents a tailored solution for processing long dialogues, significantly advancing the state-of-the-art in dialogue understanding tasks. It expands the utility of pre-trained LLMs to settings previously deemed challenging due to dialogue length and complexity. The window-based denoising strategy and hybrid attention mechanism stand out as pivotal innovations for accomplishing this task.

Looking forward, the approach laid out by DialogLM offers a promising foundation for further research into models that require similar handling of extensive contextual input, such as multi-turn conversation analysis, complex event detection, or interactive agents that simulate natural human conversation dynamics. These applications can be extended with modifications tailored to emerging requirements in natural language understanding.

Overall, by stepping beyond traditional dialogue modeling efforts limited to short-form conversations, DialogLM embodies a strategic transition towards accommodating the practical complexities found in authentic human interlocutions. As researchers continue to refine and expand upon this foundation, DialogLM may catalyze subsequent advancements in both the theoretical understanding and practical applications of AI in conversational contexts.