Coreference-Aware Dialogue Summarization (2106.08556v2)

Abstract: Summarizing conversations via neural approaches has been gaining research traction lately, yet it is still challenging to obtain practical solutions. Examples of such challenges include unstructured information exchange in dialogues, informal interactions between speakers, and dynamic role changes of speakers as the dialogue evolves. Many of such challenges result in complex coreference links. Therefore, in this work, we investigate different approaches to explicitly incorporate coreference information in neural abstractive dialogue summarization models to tackle the aforementioned challenges. Experimental results show that the proposed approaches achieve state-of-the-art performance, implying it is useful to utilize coreference information in dialogue summarization. Evaluation results on factual correctness suggest such coreference-aware models are better at tracing the information flow among interlocutors and associating accurate status/actions with the corresponding interlocutors and person mentions.

Coreference-Aware Dialogue Summarization: A Technical Overview

The paper entitled "Coreference-Aware Dialogue Summarization" makes a significant advancement in the area of abstractive dialogue summarization by proposing methodologies that incorporate coreference information into neural networks. Addressing the unique challenges posed by the unstructured and informal nature of dialogues, this paper stands out by enhancing the ability of models to accurately trace and summarize conversational exchanges involving multiple speakers and dynamic role changes.

Methodological Advancements

The paper emphasizes three primary methods for integrating coreference information into neural summarizers:

1. GNN-Based Coreference Fusion: This method employs Graph Convolutional Networks (GCN) to model the underlying structure of coreference links. By constructing a graph that characterizes entities in coreference chains, this approach aids in enhancing contextualized representations. The model addresses the complex inter-relationships of entities within dialogues, capitalizing on the capability of GNNs to handle interlinked data structures.
2. Coreference-Guided Attention: Functioning as a parameter-free enhancement, this approach utilizes an additional attention layer to incorporate coreference information without significant alterations to the existing network architecture. It updates encoded representations by applying attention mechanisms across coreferential mentions, thereby improving the model's precision in linking mentions with their antecedents.
3. Coreference-Informed Transformer: By probing attention heads and replacing specific ones with coreference-informed attention weights, this method directly enhances the self-attention mechanism in the Transformer model. This strategic modification ensures that the model pays more attention to chains of coreferential mentions, thereby enriching the encoder's output with relevant referential information.

Experimental Evaluation

The authors offer experimental results on the SAMSum dataset, demonstrating that the introduced approaches deliver state-of-the-art performance enhancements in ROUGE scores, especially in precision. Their models achieve a notable increase, indicating the models generate more concise and coherent summaries while minimizing factual inaccuracies and unnecessary redundancy.

Coreference Resolution and Preprocessing

A pivotal component of this research is the preprocessing technique employed to address dialogue coreference resolution challenges. Automatic coreference resolution was refined through post-processing strategies, including model ensemble techniques and adjustments to coreference clusters. These enhancements reportedly improved coreference assignment accuracy, reinforcing the efficacy of the proposed approach.

Implications and Future Work

The implications of this research extend beyond dialogue summarization, offering insights into improving the factual accuracy and structural coherence of generated summaries across dialogue-based NLP tasks. The paper points to potential explorations in leveraging coreference information within broader contexts, including dialogue systems, interactive virtual assistants, and multi-turn dialogue reasoning.

Given the robust experimental frameworks and promising results, future developments could focus on domain adaptation techniques to enhance coreference resolution further and explore the integration of these methods with larger-scale pre-trained models for improved linguistic comprehension in dialogue scenarios.

In conclusion, this paper provides substantial empirical evidence supporting the inclusion of coreference knowledge as a means to surpass current limitations in dialogue summarization. The methodologies proposed present a judicious balance of innovation and practicality, setting a solid foundation for continued exploration into coreference-enhanced NLP applications.