A Long Dialogue Summary Model Integrating Salience Discourse Context Window Sampling Methods
A long dialogue summary generation model with integrated salience discourse context window sampling method(SDCWS)is proposed according to the characteristics of dialogue corpus.The model is divided into two modules.1)The salience discourse context window sampling module(CWS)evaluates the dialogue discourse for salience,uses the salient discourse as the sampling anchor point,and then sets the sampling window to extract the discourse adjacent to the left and right of the sampling anchor point together as fragments,containing richer discourse relations.2)The inter-fragment information fusion summary generation module(IF)uses the transformer block to fuse information from mutually independent fragments,enhancing the semantic relationships between fragments and assigning blended weights to fragments during summary generation.The loss-of-consistency mechanism is used to encourage the salience discourse context window sampling module to determine better sampling anchors.Experimental results on the publicly available query-based long conversation summary dataset QMSum show that scores of the proposed model are significantly higher than the best existing model on the ROUGE evaluation metric.
long dialogue summarywindow samplingsalient discourseinformation fusiongenerating models