The invention discloses a short text automatic abstracting method and
system based on double encoders, belongs to the technical field of
information processing, and is characterized by comprising thefollowing steps: 1, preprocessing data, 2, designing a double
encoder with a bidirectional
recurrent neural network, and 3, arranging an attention mechanism fusing global and local
semantics 4, arranging a decoder with empirical probability distribution and using a decoder designed by adopting a double-layer unidirectional neural network; 5, adding
word embedding characteristics, 6, optimizing
word embedding dimensions, and 7, carrying out preprocessing and testing on the news corpus data from the Sogou laboratory, substituting the news corpus data into a Seq2Seq model with double encoders andaccompanying empirical probability distribution to carry out calculation, and carrying out experimental evaluation through a text abstract quality
evaluation system Rouge. According to the invention,traditional weaving is carried out; and the decoding framework is subjected to optimization research, so that the model can fully understand text
semantics, and the fluency and precision of text abstracts are improved.