Element text processing method and apparatus, electronic device, and storage medium

An electronic device, comprising:

• a processor;
• and
• a memory communicatively connected to the processor and stored with instructions executable by the processor,
• wherein the processor is e configured to perform the following actions:
• acquiring a plurality of pieces of description information and a sample abstract labeled with an element type, of a sample object;
• extracting an element embedding feature of the element type and a description embedding feature of each of the plurality of pieces of description information;
• using the element embedding feature and the description embedding feature as inputs of a joint abstract model to be trained, and using the sample abstract as an output of the joint abstract model, wherein the joint abstract model comprises a first model and a second model, and a degree of correlation between each of the plurality of pieces of description information and the element type, output from the first model, is used as an input of the second model;
• and
• training the joint abstract model based on a classification loss value of the first model and a decoding loss value of the second model, to process commodity description information of a target object to generate a commodity abstract matching a target element type.

A non-transitory computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are configured to cause a computer to perform a method for processing an element text, the method comprising:

• acquiring a plurality of pieces of description information and a sample abstract labeled with an element type, of a sample object;
• extracting an element embedding feature of the element type and a description embedding feature of each of the plurality of pieces of description information;
• using the element embedding feature and the description embedding feature as inputs of a joint abstract model to be trained, and using the sample abstract as an output of the joint abstract model, wherein the joint abstract model comprises a first model and a second model, and a degree of correlation between each of the plurality of pieces of description information and the element type, output from the first model, is used as an input of the second model;
• and
• training the joint abstract model based on a classification loss value of the first model and a decoding loss value of the second model, to process commodity description information of a target object to generate a commodity abstract matching a target element type.