SoFunction
Updated on 2025-03-03

Springboot Integrates Java DL4J to create a text summary generation system

1. Introduction

In the era of information explosion, a large amount of text data fills our lives. Whether it is news reports, academic papers or various documents, reading and understanding these long texts takes a lot of time and effort. To solve this problem, text summary generation technology came into being. This article will introduce how to use Spring Boot to integrate Java Deeplearning4j to build a text summary generation system, which can automatically extract key information from long text and generate concise summary, helping users quickly understand the main content of the text.

Text summary generation technology has important application value in the field of natural language processing. It can help users save time and improve the efficiency of information acquisition. At the same time, for news media, academic research and other fields, the text summary generation system can also improve work efficiency and reduce the workload of manual summary.

2. Technical Overview

2.1 Spring Boot

Spring Boot is a framework for quickly building standalone, production-level Spring applications. It simplifies the development process of Spring applications, provides functions such as automatic configuration, start-up dependencies and embedded servers, allowing developers to focus more on the implementation of business logic.

2.2 Java Deeplearning4j

Java Deeplearning4j (DL4J) is a Java-based deep learning library that supports a variety of deep learning algorithms, includingConvolutional Neural Network (CNN), **Recurrent Neural Network (RNN)andLong and short-term memory network (LSTM)**, etc. In this project, we will use DL4J to build a text summary generation model.

2.3 Neural Network Selection

In the text summary generation task,Recurrent Neural Network (RNN)andLong and short-term memory network (LSTM)It is a commonly used neural network model.RNNIt can process sequence data and has good adaptability to data such as text with sequence characteristics.LSTM It's a special kind ofRNN, it can solve the traditionRNNThe existing long-term dependency problem can better capture the long-term dependencies in the text. Therefore, we chooseLSTMNeural networks as text summary generation model.

2.4 Structural characteristics and selection reasons for LSTM (Long and Short-term Memory Network)

Structural features
LSTMIt is a variant of RNN, which is mainly proposed to solve the long-term dependency problem in RNN. existLSTMIn this case, a gate control mechanism has been introduced, includingInput Door、Forgotten DoorandOutput Door。Forgotten DoorIt determines what information is discarded from the cellular state, the input gate determines what new information can be added to the cellular state, and the output gate determines what information in the cellular state can be output. These gating mechanisms allow LSTMs to better control the flow of information, thereby effectively processing longer sequence data.

Reason for selection
In speech recognition, the duration of the speech signal may be relatively long, and there are dependencies within a long time range. For example, the pronunciation of a word may be affected by the pronunciation of the before and after words. LSTM's gating mechanism can capture this long-term dependency well and improve the accuracy of speech recognition.

3. Dataset format

3.1 Dataset Source

We can use public text summary datasets such asCNN/Daily MailDataset,New York Times Annotated Corpuswait. These datasets contain a large number of news articles and corresponding digests, which can be used to train and evaluate text digest generation models.

3.2 Dataset format

Datasets are usually stored in text files, each containing a news article and a corresponding summary. The article and the abstract can be separated by a specific separator, such as "=============". Here is an example of a dataset file:

This is a news article. It contains a lot of information.
=========
This is the summary of the news article.

3.3 Data preprocessing

Before using the dataset, we need to preprocess the data. The steps of preprocessing include text cleaning, word segmentation, word vectorization, etc. Text cleaning can remove noise and useless information in text, such as HTML tags, special characters, etc. Word participle is to divide the text into words or phrases for easier subsequent processing. Word vectorization is to convert words or phrases into vector representations to facilitate processing of neural networks.

4. Technology implementation

4.1 Maven dependencies

In the project, we need to add the following Maven dependencies:

<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-core</artifactId>
    <version>1.0.0-beta7</version>
</dependency>
<dependency>
    <groupId>org.deeplearning4j</groupId>
    <artifactId>deeplearning4j-nlp</artifactId>
    <version>1.0.0-beta7</version>
</dependency>
<dependency>
    <groupId></groupId>
    <artifactId>spring-boot-starter-web</artifactId>
</dependency>

4.2 Building a model

We can use DL4JRecurrentNetworkClass to build LSTM models. Here is a sample code for building an LSTM model:

import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org..Nd4j;
import org.;
public class TextSummarizer {
    private MultiLayerNetwork model;
    public TextSummarizer(int inputSize, int hiddenSize, int outputSize) {
        // Build neural network configuration        MultiLayerConfiguration conf = new ()
               .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
               .updater(new org.())
               .list()
               .layer(0, new ().nIn(inputSize).nOut(hiddenSize).activation().build())
               .layer(1, new ()
                       .activation().nIn(hiddenSize).nOut(outputSize).build())
               .pretrain(false).backprop(true).build();
        // Create a neural network model        model = new MultiLayerNetwork(conf);
        ();
    }
    public INDArray predict(INDArray input) {
        return (input);
    }
}

In the above code, we first build aMultiLayerConfigurationObject, used to configure the structure and parameters of a neural network. Then, we useMultiLayerNetworkThe class creates an LSTM model and usesinitMethod initializes the parameters of the model. Finally, we implemented apredictMethod, used to predict the input text and generate a summary.

4.3 Training the model

After building the model, we need to use the dataset to train the model. Here is a sample code for training a model:

import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org..Nd4j;
import org.;
import ;
import ;
public class TextSummarizerTrainer {
    private TextSummarizer summarizer;
    public TextSummarizerTrainer(int inputSize, int hiddenSize, int outputSize) {
        summarizer = new TextSummarizer(inputSize, hiddenSize, outputSize);
    }
    public void train(List&lt;String&gt; articles, List&lt;String&gt; summaries) {
        // Data preprocessing        List&lt;INDArray&gt; inputs = new ArrayList&lt;&gt;();
        List&lt;INDArray&gt; targets = new ArrayList&lt;&gt;();
        for (int i = 0; i &lt; (); i++) {
            String article = (i);
            String summary = (i);
            INDArray input = preprocess(article);
            INDArray target = preprocess(summary);
            (input);
            (target);
        }
        // Create a dataset iterator        ListDataSetIterator iterator = new ListDataSetIterator(inputs, targets);
        // Training the model        for (int epoch = 0; epoch &lt; 100; epoch++) {
            (iterator);
            ("Epoch " + epoch + " completed.");
        }
    }
    private INDArray preprocess(String text) {
        // Text preprocessing logic, such as word segmentation, word vectorization, etc.        return null;
    }
}

In the above code, we first create aTextSummarizerTrainerClass, used to train text summary generation model. existtrainIn the method, we first preprocess the input article and abstract and convert it into a vector representation that the neural network can process. Then, we created aListDataSetIteratorObject, used to iterate the dataset. Finally, we usefitThe method trains the model and iterates 100 times.

4.4 Spring Boot Integration

To integrate the text summary generation model into the Spring Boot application, we can create a RESTful API that receives articles input by users and returns the generated summary. Here is a sample code for a Spring Boot controller:

import org.;
import org.;
import ;
import ;
import ;
import ;
@RestController
public class TextSummarizerController {
    private MultiLayerNetwork model;
    @Autowired
    public TextSummarizerController(MultiLayerNetwork model) {
         = model;
    }
    @PostMapping("/summarize")
    public String summarize(@RequestBody String article) {
        // Data preprocessing        INDArray input = preprocess(article);
        // Prediction summary        INDArray output = (input);
        // Post-processing, convert vectors into text summary        return postprocess(output);
    }
    private INDArray preprocess(String text) {
        // Text preprocessing logic, such as word segmentation, word vectorization, etc.        return null;
    }
    private String postprocess(INDArray output) {
        // Postprocessing logic, convert vectors into text summary        return null;
    }
}

In the above code, we create aTextSummarizerControllerClass, used to process user requests. existsummarizeIn the method, we first preprocess the article input by the user and convert it into a vector representation that the neural network can process. We then use the model to predict the input and generate the digest vector. Finally, we postprocess the digest vector, convert it into a text digest, and return it to the user.

V. Unit Test

To ensure the correctness of the text summary generation system, we can write unit tests to test the training and prediction functions of the model. Here is a sample code for a unit test:

import org.;
import ;
import ;
import org.;
import ;
import ;
import ;
import ;
import static ;
@SpringBootTest
class TextSummarizerControllerTest {
    @Autowired
    private MultiLayerNetwork model;
    private List&lt;String&gt; articles;
    private List&lt;String&gt; summaries;
    @BeforeEach
    void setUp() {
        articles = new ArrayList&lt;&gt;();
        summaries = new ArrayList&lt;&gt;();
        ("This is a news article. It contains a lot of information.");
        ("This is the summary of the news article.");
    }
    @Test
    void testSummarize() {
        String article = (0);
        String expectedSummary = (0);
        // Data preprocessing        INDArray input = preprocess(article);
        // Prediction summary        INDArray output = (input);
        // Post-processing, convert vectors into text summary        String actualSummary = postprocess(output);
        assertEquals(expectedSummary, actualSummary);
    }
    private INDArray preprocess(String text) {
        // Text preprocessing logic, such as word segmentation, word vectorization, etc.        return null;
    }
    private String postprocess(INDArray output) {
        // Postprocessing logic, convert vectors into text summary        return null;
    }
}

In the above code, we first create aTextSummarizerControllerTestClass, used to test the functionality of the text summary generation system. existsetUpIn the method, we initialize some test data, including the article and the corresponding summary. existtestSummarizeIn the method, we first preprocess the test article and convert it into a vector representation that the neural network can process. We then use the model to predict the input and generate the digest vector. Finally, we postprocess the digest vector, convert it into a text summary, and compare it with the expected digest.

VI. Expected output

When we run the text summary generation system, we can expect the following output:

During the training process, the system will output the training progress and loss value of each epoch. For example:

Epoch 0 completed. Loss: 0.5
Epoch 1 completed. Loss: 0.4
...
Epoch 99 completed. Loss: 0.1

When we send an article to the system, the system returns the generated summary. For example:

{
    "article": "This is a news article. It contains a lot of information.",
    "summary": "This is the summary of the news article."
}

7. References

  • Deeplearning4j Documentation
  • Spring Boot Documentation
  • Text Summarization with Deep Learning

This is the article about Springboot integrating Java DL4J to create a text summary generation system. For more related contents of Springboot integrating Java DL4J text summary generation system, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!