Study on Pre-training Tasks for Multi-document Summarization
News summarization aims to quickly and accurately extract a concise summary from the complex news text.This paper studies the multi-document summary based on the pre-training language model,focusing on the effect of model training methods combined with pre-training tasks on improving model performance,and strengthening information exchange between multiple documents to generate more comprehensive and brief summaries.For combined pre-training tasks,this paper conducts compara-tive experiments on the baseline model,pre-training task content,pre-training task quantity,and pre-training task order,explores and marks effective pre-training tasks,summarizes the specific methods to strengthen the information exchange between docu-ments,and refines and proposes a concise and efficient pre-training process.Through training and testing on the public news multi-document dataset,experimental results show that the content,quantity,and order of the pre-training tasks have a certain improvement on the ROUGE value,and the specific pre-training combination proposed by integrating the conclusions of the three has a significant increase in the ROUGE value.
NETL
NSTL
万方数据
