Automatic text summarization method, apparatus, computer device, and storage medium

A computer device, comprising a memory, a processor and a computer program stored in the memory and operated at the processor, characterized in that the processor executes the computer program with the steps of:

• obtaining a character included in a target text sequentially, and decoding the character according to a first-layer long short-term memory (LSTM) structure inputted into a LSTM model sequentially to obtain a sequence composed of hidden states, wherein the LSTM model is a LSTM neural network;
• inputting the sequence composed of hidden states into a second-layer LSTM structure of the LSTM model and decoding the sequence composed of hidden states to obtain a word sequence of a summary;
• inputting the word sequence of the summary into the first-layer LSTM structure of the LSTM model and decoding the word sequence of the summary to obtain an updated sequence composed of hidden states;
• obtaining a context vector corresponding to a contribution value of a hidden state of a decoder according to the contribution value of the hidden state of the decoder in the updated sequence composed of hidden states;
• and
• obtaining a probability distribution of a word in the updated sequence composed of hidden states according to the updated sequence composed of hidden states and the context vector, and outputting the most probable word in the probability distribution of the word as a summary of the target text.

A non-transitory computer-readable storage medium, for storing a computer program, and the computer program comprising a program instruction, wherein when the program instruction is executed by a processor, the processor executes the operations of:

• obtaining a character included in a target text sequentially, decoding the character according to a first-layer long short-term memory (LSTM) structure inputted into a LSTM model sequentially to obtain a sequence composed of hidden states, wherein the LSTM model is a LSTM neural network;
• inputting the sequence composed of hidden states into a second-layer LSTM structure of the LSTM model and decoding the sequence composed of hidden states to obtain a word sequence of a summary;
• inputting the word sequence of the summary into the first-layer LSTM structure of the LSTM model and decoding the word sequence of the summary to obtain an updated sequence composed of hidden states;
• obtaining a context vector corresponding to a contribution value of a hidden state of a decoder according to the contribution value of the hidden state of the decoder in the updated sequence composed of hidden states;
• and
• obtaining a probability distribution of a word in the updated sequence composed of hidden states according to the updated sequence composed of hidden states and the context vector, and outputting the most probable word in the probability distribution of the word as a summary of the target text.