Abstract: While the development of social networks brings convenience,but also generates massive amounts of chat data.How to filter key information from chat conversations has become a major difficulty.Chat summary is an effective tool to solve such pro-blems,as it allows users to quickly obtain important content without having to repeatedly browse through lengthy chat records.Currently,pre-trained models are widely used in various types of text,including unstructured,semi-structured,and structured text.However,for chat dialogue text,common pre-trained models are often unable to capture its unique structural features,and further exploration and improvement are still needed.To address these issues,this paper proposes a chat summary model MGCSum,which based on multi-granularity contrastive learning and does not require manual annotation of the datasets,making it easy to learn and transfer.Firstly,a stop word list for chat text is constructed by using document frequency,term frequency and entropy to remove interference information in chat.Then,self-supervised contrastive learning is performed at the granularity of words and topics to identify the structure of conversation,uncover keywords and distinct topic information in chats.Experimental results on the publicly available chat summary datasets SAMSum and financial fraud dialogue summary dataset FINSum show that,compared to current mainstream chat summary methods,this algorithm significantly improves coherence,information content and ROUGE evaluation metrics.

Key words: Chat summary, Contrastive learning, Pre-trained models, Keyword detection, Topic segmentation