  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.
