Abstract generation method based on planning mechanism and knowledge graph guidance

A knowledge graph and mechanism technology, applied in neural learning methods, computing, biological neural network models, etc., can solve problems such as word order, logical contradictions and repetition of generative abstracts, and achieve the effect of semantic fluency

A knowledge graph and mechanism technology, applied in neural learning methods, computing, biological neural network models, etc., can solve problems such as word order, logical contradictions and repetition of generative abstracts, and achieve the effect of semantic fluency

CN112597296AActive Publication Date: 2021-04-02SUN YAT SEN UNIV
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  • Abstract generation method based on planning mechanism and knowledge graph guidance
  • Abstract generation method based on planning mechanism and knowledge graph guidance
  • Abstract generation method based on planning mechanism and knowledge graph guidance

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Embodiment Construction

[0051] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0052] In order to better illustrate this embodiment, some parts in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0053] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0054] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] Such as figure 1 As shown, a summary generation method based on planning mechanism and knowledge map guidance is applied to a summary generation system based on planning mechanism and knowledge map guidance. The system includes a semantic encoder, a planning mechanism module, and a summary decoder and a content understander, including the following steps:

[0056] S1: The semantic enc...

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Abstract

The invention provides an abstract generation method based on a planning mechanism and knowledge graph guidance. The method comprises the steps: directly taking a knowledge graph as the input of a model, extracting attention features from the uninterrupted global semantic features of the knowledge graph in an abstract decoding process, and maintaining the semantic consistency of the context of anabstract; compared with a text generation model adopting a graph neural network as an encoder, the method has the advantages that entity information covered by each sentence segment in the abstract isannotated in a supervised manner, so that a planning mechanism module of the training model can capture semantic features of a generated text before the sentence segments are generated; therefore, the sub-graphs of the knowledge graph are further explicitly extracted as fine-grained guidance, so that the model is guided to generate abstract texts with smoother semantics and more self-consistent logic.

Description

technical field [0001] The present invention relates to the field of artificial intelligence algorithms, and more specifically, to a method for generating summaries based on planning mechanisms and knowledge map guidance. Background technique [0002] Information on the Internet is growing exponentially, providing various content and information to hundreds of millions of Internet users. Users expect to be able to quickly and conveniently obtain the main information and read the abstract. However, at present, the abstract text generation and refinement of various long articles on the Internet are mainly completed by professional editors, which is not only time-consuming and labor-intensive, but also unsatisfactory. market demand. [0003] In recent years, with the development of artificial intelligence and big data, machine writing and text information summarization technology has made remarkable progress. Abstract automatic generation technology is gradually commercialize...

Claims

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Application Information

Patent Timeline
02 Apr 2021
Publication
CN112597296A
IPC
G06F16/34; G06F16/36; G06F40/295; G06F40/30; G06N3/04; G06N3/08
CPC
G06F16/345; G06F16/367; G06F40/295; G06F40/30; G06N3/08; G06N3/045
Inventors
林镇坤; 苏勤亮