To generate summaries that include multiple aspects or topics for text
documents, most approaches use clustering or topic modeling to group relevant
sentences and then generate a summary for each group. These approaches struggle
to optimize the summarization and clustering algorithms jointly. On the other
hand, aspect-based summarization requires known aspects. Our solution
integrates topic discovery and summarization into a single step. Given text
data, our Joint Aspect Discovery and Summarization algorithm (JADS) discovers
aspects from the input and generates a summary of the topics, in one step. We
propose a self-supervised framework that creates a labeled dataset by first
mixing sentences from multiple documents (e.g., CNN/DailyMail articles) as the
input and then uses the article summaries from the mixture as the labels. The
JADS model outperforms the two-step baselines. With pretraining, the model
achieves better performance and stability. Furthermore, embeddings derived from
JADS exhibit superior clustering capabilities. Our proposed method achieves
higher semantic alignment with ground truth and is factual.

通过将主题发现和总结集成到单一步骤中，我们提出的 JADS 算法从文本数据中发现方面，并生成主题的总结，其在优化总结和聚类算法中表现出更好的性能和稳定性，此外，从 JADS 中获得的嵌入展现出更优越的聚类能力，我们所提出的方法在语义对齐和准确性方面取得了较高的水平。