Pre-training language model-based summarization generation method

A pre-training language model-based summary generation method, the method comprising:

• acquiring text information of a summary to be generated, and performing a language pre-training process having a multi-feature weight on the text information to obtain a candidate summary;
• wherein the multi-feature weight comprises a plurality of dimension weighted feature data, and the plurality of dimension weighted feature data comprises:
• a corresponding sentence similarity calculation value, a title similarity weighting value, a keyword weighting value, a subject term weighting value, a position information weighting value, and a KNN smoothing strategy value;
• inputting the candidate summary into a pre-training language model to obtain a pre-training language model output data, wherein the pre-training language model is generated according to a first modeling model, and a parameter setting of the first modeling model comprises:
• setting a size of a training batch, a text maximum length, a maximum length of a target summary, and a bundling size;
• and
• inputting the pre-training language model output data into a decoder model and obtaining a target summary, wherein a number of a plurality of layers in a decoder of the decoder model is a preset value.