Text generation learning method, text generation method, text generation learning apparatus, text generation apparatus and program

A text generation method performed by a computer, the text generation method comprising:

• receiving, as inputs, a first source text and an instruction related to a summary of the first source text, generating instruction features, which are a sequence of vectors for respective tokens constituting the instruction, and generating first text features, which are a sequence of vectors for respective tokens of the instruction and the first source text, based on the first source text and the instruction features;
• receiving the first text features as an input and outputting first probability distribution of a text;
• outputting a first text based on the first probability distribution;
• generating, in response to receiving a second source text as an input, second text features, which are a sequence of vectors for respective tokens of the instruction and the second source text, based on the second source text and the instruction features;
• receiving the second text features as an input and outputting second probability distribution of a text;
• and
• outputting a second text based on the second probability distribution.

A text generation method performed by a computer, the text generation method comprising:

• receiving, as inputs, a source text and a first instruction related to a summary of the source text, generating source text features, which are a sequence of vectors for respective tokens constituting the source text, and generating first text features, which are a sequence of vectors for respective tokens of the first instruction and the source text, based on the first instruction and the source text features;
• receiving the first text features as an input and outputting first probability distribution of a text;
• outputting a first text based on the first probability distribution;
• generating, in response to receiving a second instruction as an input, second text features, which are a sequence of vectors for respective tokens of the second instruction and the source text, based on the second instruction and the source text features;
• receiving the second text features as an input and outputting second probability distribution of a text;
• and
• outputting a second text based on the second probability distribution.