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A Generative Conference Summarization Method Based on Graph Convolutional Neural Networks

A convolutional neural network and conference abstract technology, applied in the field of generative conference abstracts, which can solve the problem of ignoring the structural information of dialogue chapters

Active Publication Date: 2021-08-10
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the existing method only uses the sequence structure of sentences and words to model the conference text, ignoring the rich dialogue structure information of the conference, and proposes a generative meeting summary method that incorporates dialogue structure information

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  • A Generative Conference Summarization Method Based on Graph Convolutional Neural Networks
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  • A Generative Conference Summarization Method Based on Graph Convolutional Neural Networks

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specific Embodiment approach 1

[0028] Specific implementation mode one: combine figure 1 Describe this implementation mode. In this implementation mode, a generative conference summarization method based on graph convolutional neural network has a specific process as follows:

[0029] Step 1: Train the dialogue discourse structure parser, and mark the meeting data to obtain the dialogue discourse structure of the meeting;

[0030] Step 2: For each meeting in the meeting data set and the dialogue chapter structure of the meeting obtained in step 1, construct a meeting chapter structure diagram. The meeting chapter structure diagram includes all sentences in the meeting, all participants in the meeting, and Dialogue discourse structure between sentences;

[0031] Denote the conference chapter structure graph as G D =(V D ,E D , R D ), where v i ∈V D represent graph nodes, (v i ,r,v j )∈E D Represents the edge in the graph, r∈R D Represents the edge relationship in the graph;

[0032] Step 3: Use t...

specific Embodiment approach 2

[0038] Embodiment 2: This embodiment differs from Embodiment 1 in that: in the step 1, train the dialogue text structure parser, and mark the meeting data to obtain the dialogue text structure of the meeting; the specific process is:

[0039] Step 11. Use the existing STAC dataset [5] (Title: Discourse structure and dialogueacts in multiparty dialogue: The stac corpus, Authors: Nicholas Asher, Julie Hunter, Mathieu Morey, Farah Benamara, and Stergos Afantenos, Year: 2016) Training the existing dialogue structure parser Deep Sequential [6] (Title: Adeep sequential model for discourse parsing on multi-party dialogues, author: Zhouxing Shi and Minlie Huang, time: 2019, literature quoted from: Proceedings of the AAAI Conference on Artificial Intelligence), get the trained dialogue text structure parser Deep Sequential;

[0040] Step 1 and 2: Use the trained dialogue discourse structure parser Deep Sequential to mark the dialogue discourse structure of the AMI meeting data, and obt...

specific Embodiment approach 3

[0042] Specific embodiment three: what this embodiment is different from specific embodiment one or two is: utilize existing STAC data set to train existing dialogue discourse structure parser Deep Sequentia in the described step one by one, obtain the dialogue discourse structure analysis that has trained Deep Sequential; the specific process is:

[0043] The STAC dataset is a multiplayer chat dataset for English games. This corpus marks the semantic relationship between the basic semantic units (EDU) in the dialogue;

[0044] Said multiple persons are 3 or more persons.

[0045] According to statistics, STAC contains a total of 1091 dialogues, 10677 basic semantic units, and 11348 relational instances;

[0046] According to the task definition, at least 3 speakers participated in each dialogue. Each message sent by each speaker is usually 1-2 sentences each time, and each sentence is usually regarded as an elementary semantic unit (EDU) in multi-person dialogue discourse a...

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Abstract

A generative conference abstract method based on graph convolutional neural network, the invention relates to a generative conference abstract method based on graph convolutional neural network. The purpose of the present invention is to solve the problem that the existing methods only use the sequence structure of sentences and words to model conference texts, ignoring the rich dialogue text structure information of conferences. The process is as follows: 1: Get the dialogue text structure of the meeting; 2: Construct the meeting text structure diagram, and the dialogue text structure between sentences in the meeting; 3: Construct the pseudo-data and the corresponding pseudo-data conference text structure diagram; 4: Obtain The pre-trained graph neural network generative meeting summary model and initialization parameters; get the trained graph neural network generative meeting summary model and model parameters; use the trained graph neural network generative meeting summary model to be tested The meeting conducts tests and generates summaries. The invention is used for a generation conference summarization method in the field of natural language processing.

Description

technical field [0001] The present invention relates to a generative meeting summarization method based on graph convolutional neural network. Background technique [0002] Based on natural language processing - automatic text summarization (Automatic Summarization) [1] (Title: Constructing literature abstracts by computer: techniques and prospects, author: Chris D Paice, year: 1990, literature cited from Information Processing & Management) under the field of abstractive meeting summarization (Abstractive Meeting Summarization), that is, the text of a given multi-person meeting Record, generate a short text description containing key information of the meeting, as shown in Figure 2(a), which shows a meeting fragment and its corresponding standard summary. [0003] For conference summaries, existing methods can be divided into two categories: extractive and abstractive. The extraction method selects important sentences from the meeting minutes to form the final abstract. A...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/34G06N3/04G06N3/08
CPCG06F16/345G06N3/08G06N3/045
Inventor 冯骁骋秦兵冯夏冲刘挺
Owner HARBIN INST OF TECH
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