Modeling Unified Semantic Discourse Structure for High-quality Headline Generation

Conceitos Básicos

Utilizing a unified semantic discourse structure improves headline generation by capturing core document semantics effectively.

Resumo

The article introduces a method using a unified semantic discourse structure (S3) to represent document semantics, combining RST trees and AMR graphs. The hierarchical composition of sentence, clause, and word characterizes the semantic meaning. A headline generation framework is developed using S3 graphs as contextual features, with a dynamic pruning mechanism to enhance efficacy. Experimental results show outperformance on headline generation datasets.

Introduction

Headline generation aims to summarize documents concisely.
Research has shifted focus to truthfulness and attractiveness in headlines.
Existing methods overlook intrinsic document characteristics.

Related Work

Automatic headline generation has received significant research attention.
Methods classified into extractive and abstractive paradigms.
Abstractive methods achieve state-of-the-art performance.

Discourse Structure Modeling

RST trees and AMR graphs are integrated into an S3 graph for document representation.
Hierarchical structure pruning mechanism enhances the efficacy of the discourse structure.

Headline Generation Framework

PLM encodes input documents for contextual representations.
GAT models the S3 graph features for structural modeling.
Dynamic structure pruning filters redundant nodes based on reinforcement learning.

Experimental Settings

Experiments conducted on CNNDM-DH and DM-DHC datasets.
Comparison with strong-performing baseline models shows superior performance across metrics.

Results and Discussion

Our model outperforms baselines consistently on headline generation tasks.
Human evaluation confirms high-quality generated headlines compared to baselines.

Further Analyses

Impact of document length shows our model's advantage in handling longer documents effectively.
Roles of different node types in the S3 graph highlight the importance of key information nodes after pruning.

Estatísticas

"Experimental results demonstrate that our method outperforms existing state-of-art methods consistently."

Citações

"Our work can be instructive for a broad range of document modeling tasks."
"Document texts consist of a considerable number of subordinate sentences or clauses, thus containing lengthy and mixed information."

Principais Insights Extraídos De

Modeling Unified Semantic Discourse Structure for High-quality Headline Generation

by Minghui Xu,H... às arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15776.pdf

Perguntas Mais Profundas

How does the proposed hierarchical pruning mechanism impact model performance?

The proposed hierarchical pruning mechanism plays a crucial role in enhancing model performance. By dynamically filtering out redundant and nonessential nodes within the discourse structure graph, the pruning mechanism helps to focus on essential information for headline generation. This results in a more compact yet highly effective representation of document semantics, leading to improved headline quality. The pruning process optimizes the graph structure by removing noise and irrelevant details, allowing the model to concentrate on key features that contribute to generating high-quality headlines.

What are potential limitations or challenges in applying this method to other document modeling tasks?

While the hierarchical pruning mechanism shows promise for improving headline generation tasks, there are potential limitations and challenges when applying this method to other document modeling tasks:

Complexity of Document Structures: Different types of documents may have varying structures that require tailored approaches for effective pruning.
Scalability: Scaling this method to larger datasets or more complex documents could pose computational challenges.
Generalizability: The effectiveness of the hierarchical pruning mechanism may vary across different domains or text genres.
Annotation Requirements: Constructing unified semantic discourse structures (S3) requires annotated data, which may not be readily available for all document types.

How might advancements in neural language models affect the relevance and applicability of this approach in the future?

Advancements in neural language models can significantly impact the relevance and applicability of approaches like hierarchical pruning mechanisms for document modeling tasks:

Improved Performance: Enhanced language models can provide better contextual understanding, leading to more accurate semantic representations during graph encoding.
Efficiency: More efficient training algorithms and architectures can optimize dynamic structure pruning processes, making them faster and more effective.
Adaptability: Advanced models can adapt better to diverse document structures and lengths, improving their applicability across various domains.
Incorporation of External Knowledge: Future models may integrate external knowledge sources seamlessly with structured data representations like S3 graphs, further enhancing their capabilities.

These advancements will likely lead to even greater improvements in headline generation accuracy and efficiency while expanding the scope of applications beyond summarization tasks into broader areas of natural language processing research.