Integrating Concept Associations for Query Focused Knowledge Summarization

Knowledge summarization task aims to summarize the knowledge scattered in multiple documents for answering a query. In this paper, we adopt the concept relation knowledge base ConceptNet for the task and propose the ConceptNet integrated summarization method CNSum, where the concepts are adopted as a bridge to find the latent associations between the query and segments in documents. Besides, a semantic mixture mechanism is introduced to combine the concept-centered associations with the contextual semantics of segments for summarization. To evaluate the knowledge in summary without references, we introduce a Question Answer (QA) based knowledge labeling method to construct training samples. The training samples are used for training a neural evaluation model. We compare CNSum with multiple methods and large language models (LLMs). The results show that CNSum outperforms these baselines. We also evaluate the knowledge in the generated summaries by human evaluation and our neural evaluation. The results show that CNSum is also better than baselines on knowledge completeness. Besides, these two evaluation results are highly correlated.