Multi-text abstract generation method, device, server and storage medium
A text and abstract technology, applied in the fields of devices, servers, storage media, and multi-text abstract generation methods, can solve problems such as unnaturalness, poor overall quality of the abstract, and unsmooth content of the abstract, etc.
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Embodiment 1
[0029] figure 1 It is a flow chart of a method for generating a multi-text abstract provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation where a multi-text abstract needs to be generated. The method can be executed by a corresponding multi-text abstract generating device, which can adopt It can be realized by means of software and / or hardware, and can be configured on a server.
[0030] Such as figure 1 As shown, the multi-text abstract generation method provided in the embodiment of the present invention may include:
[0031] S110. Determine a summary sentence set corresponding to the target text set from the sentences of each text in the target text set.
[0032] Among them, the target text set includes at least two texts, and to generate a high-quality summary corresponding to the target text set, the summary must cover enough important information provided by each text, that is, the summary of the target text set It is compos...
Embodiment 2
[0042] figure 2 It is a schematic flowchart of a method for generating a multi-text abstract provided in Embodiment 2 of the present invention. This embodiment is optimized on the basis of the above embodiments, such as figure 2 As shown, the multi-text abstract generation method provided in the embodiment of the present invention may include:
[0043] S210. Text preprocessing.
[0044] In order to ensure that the text data for generating summaries is clean, it is necessary to preprocess each text in the target text set to filter out useless data included in the target text, and because too long input text will affect the operating efficiency of the model, and too long The text generation summary effect is not good, and the long text needs to be preprocessed. Exemplarily, text preprocessing may include the following processing operations:
[0045] (1) Use regular expressions to match, filter webpage links in the target text, for example, match a string through regular ex...
Embodiment 3
[0054] image 3 It is a schematic flowchart of a method for generating a multi-text abstract provided by Embodiment 3 of the present invention. This embodiment is optimized on the basis of the above embodiments, such as image 3 As shown, the multi-text abstract generation method provided in the embodiment of the present invention may include:
[0055] S310. Calculate the importance score of each sentence of each text in the target text set based on the graph ranking model, where the target text set includes at least two texts.
[0056] In this embodiment, the importance score of each sentence can be calculated by using the TextRank model based on graph ranking. Based on the TextRank model, each sentence is regarded as a node in the graph. If there is similarity between two sentences, it is considered that there is an undirected weighted edge between the corresponding two nodes, and the weight of the edge is the similarity. The most important sentences calculated by the Pag...
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