Overview of automatic summarization techniques and examples of algorithms and implementations

Automatic Summarization Technology

Automatic summarization technology is widely used in information retrieval, information processing, natural language processing, machine learning, and other fields to compress large text documents and sentences into a short, to-the-point form that is easy to understand.

Automatic summarization technology can be broadly divided into two types: extraction-based summarization and abstraction-based summarization.

Extractive summarization is a method of generating a summary by extracting important phrases or sentences from the original document. This approach tends to retain important information in the text and generally has less information missing. The basic algorithm for extractive summarization would be to use an algorithm that calculates an importance score for a word or phrase and selects the most important parts.

Abstractive summarization would be a method that generates new sentences to summarize the content of the original text. This method is more similar to human summarization because it describes the content of the text in its own words, allowing for different ways of expressing information, but can be more challenging because it uses natural language generation techniques.

Automatic summarization technology is positioned as part of NLP, a technology that enables computers to understand and generate natural language, and summarization is one of its applications NLP models and algorithms can help analyze text, understand context, and generate summaries, and can be used in information search engines, news aggregation, review summaries, research article summarization, automatic generation of client reports, and many other areas.

Automatic summarization technology is also an important tool in an era of information overload, helping people process and understand information efficiently.

Extractive summarization is discussed in detail next.

Extractive Summaries

<Overview>

Extractive Summarization is a natural language processing task that extracts important information from a text document or sentence and presents it as a summary sentence. In this method, a sentence or part of a sentence is selected from the original document and assembled into a summary sentence. The features of extractive summarization are as follows

1. Sentence selection: Extractive summarization uses a method of selecting sentences or sentence fragments from the original document. The selected sentences are the parts of the original document that are considered important.
2. Importance Rating: Sentence selection is done by an algorithm that evaluates the importance of each sentence. Common approaches include frequency of keywords in the sentence, location of the sentence, length of the sentence, and content of the sentence.
3. Relative summarization: Extractive summarization presents sentences extracted from the original document as summary sentences, thus maintaining a relationship between the original document and the summary sentences. The summary text is a quotation from the original document.
4. Automation: Extractive summarization is an approach that can be easily automated and can summarize large numbers of documents in a short period of time. This approach is useful in many applications such as information retrieval, information gathering, and summary article generation.

<Algorithm Used for Extractive Summarization>

There are various algorithms for extractive summarization that evaluate the importance of sentences and select important sentences. The following are the main algorithms.

1. TF-IDF (Term Frequency-Inverse Document Frequency):

TF-IDF, described in “Overview of tfidf and its implementation in Clojure” is a method to evaluate the importance of a sentence by combining the frequency of occurrence of each word in the sentence (Term Frequency) and the importance of the word in the entire document set (Inverse Document Frequency). The sentence with the highest TF-IDF score for a word is selected as the most important sentence.

2. Latent Semantic Analysis (LSA):

LSA, also described in “Overview and Various Implementations of Topic Models” analyzes the relationship between documents and words and selects sentences based on the semantic similarity of the sentences. By projecting documents into a low-dimensional potential semantic space and calculating similarity, important sentences can be extracted.

3. TextRank:.

TextRank is a graph-based algorithm that identifies important sentences by constructing a graph with nodes representing sentences and edges representing similarities between sentences, and then using an algorithm similar to PageRank, which is also described in “Overview and Implementation of the PageRank Algorithm.

4. Part-of-Speech Filtering:

Part-of-Speech filtering generates a summary by selecting sentences with a particular part-of-speech (noun, verb, adjective, etc.). Part-of-speech filtering is useful for generating summaries that focus on the subject or action of a sentence. Part-of-Speech filtering is performed using approaches such as “Overview of Relational Data Learning with Examples and Implementations“.

5. neural network models:

Recent approaches include the use of neural network models such as recurrent neural networks (RNNs), transformers, and BERT to predict sentence importance. These models are trained from large textual data sets and help to more accurately understand the context and meaning of a sentence. (For more information on neural network approaches, see “python Keras Overview and Examples of Application to Basic Deep Learning Tasks,” etc.)

<Example Implementation>

Extractive summarization is typically implemented by combining several libraries and tools using Python. Below are the basic steps and example code for implementing extractive summarization using Python.

1. Import libraries: First, import the libraries you need. Typically, use a natural language processing library such as Natural Language Toolkit (NLTK), Gensim, or spaCy.

import nltk
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.probability import FreqDist

2. Text preprocessing: Reads the text to be summarized and preprocesses it. Preprocessing includes tokenizing the text, removing stop words, removing punctuation, etc.

nltk.download('stopwords')
nltk.download('punkt')

text = "Enter the text to be summarized here."
sentences = sent_tokenize(text)
words = [word.lower() for word in word_tokenize(text) if word.isalnum() and word not in stopwords.words('english')]

3. Calculating the importance of a word: Calculate the importance of a word to determine the score for each sentence. In this example, TF-IDF is used to calculate importance.

from sklearn.feature_extraction.text import TfidfVectorizer

tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(sentences)

4. Calculate the score of each sentence: Calculate the score of each sentence and select the sentence with the higher score.

sentence_scores = {}
for i in range(len(sentences)):
    sentence_scores[i] = sum(tfidf_matrix[i].toarray()[0])

# Sort sentences in descending order of score
sorted_sentences = sorted(sentence_scores.items(), key=lambda x: x[1], reverse=True)

5. Extractive summary generation: Generates summary sentences by extracting selected sentences in order of increasing score.

num_sentences_to_extract = 3  # Specify the number of sentences to extract
selected_sentences = [sentences[i] for i, _ in sorted_sentences[:num_sentences_to_extract]]
summary = " ".join(selected_sentences)
print(summary)

In this code example, TF-IDF is used to calculate the importance of sentences and select high-scoring sentences to generate summary sentences. In actual summarization tasks, it is important to adjust preprocessing, algorithm selection, and length of summary sentences according to text data and summarization needs.

<Extractive Summarization Challenges>

There are several challenges associated with extractive summarization. The main challenges are described below.

1. Sentence selection accuracy:

Extractive summarization relies on sentence selection. Because it is difficult to accurately assess the importance of sentences, sometimes important information is overlooked or unnecessary information is included. Particularly in complex or specialized documents, the accuracy of selection is easily compromised.

2. lack of context:

Because extractive summarization presents selected sentences as-is as summary sentences, it may lack sentence connections and context. This can make the summary text difficult for the reader to understand.

3. redundancy:

The same information may be included in multiple sentences to emphasize important information, making the summary redundant. This hinders effective summarization.

4. handling unknown information:

When a document contains new topics or unknown information, extractive summarization may not be able to recognize it and summarize it appropriately. This may reduce the reliability of the summary.

5 Language Dependency:

Extractive summarization is generally language-dependent and requires language-specific methods and tools to apply it to documents in different languages.

6. processing long documents:

Extractive summarization of long documents is difficult, and the computational and processing time constraints become an issue when the number of sentences is large. In addition, the selection of sentences from long documents can easily lead to errors.

To address these issues, various methods for improvement have been studied. These are described below.

<Proposed solutions to the problems of extractive summarization>

Proposals to address the issues of extractive summarization are considered in order to improve the quality and efficiency of summarization. These are described below.

1. Improvement of Extraction Algorithm:

In order to improve the quality of summary, more advanced extraction algorithms could be employed. For example, machine learning models could be used to more precisely evaluate the importance of sentences.

2. utilize linguistic models:

Modern language models (e.g., BERT, GPT series) can be used to generate context-sensitive summaries. This allows for more natural summaries.

3. setting summary evaluation criteria:

Clear criteria can be set for evaluating the quality of summaries, and automated evaluations can be performed based on these criteria. This can be done using metrics such as ROUGE and BLEU.

4. understanding user needs:

It is important to tailor the summary to the type and length of summary that the user requires, which requires customization from a general summary to a summary tailored to their specific needs.

5. multi-language support:

A summarization system can be built to support multiple languages so that summaries can be provided in different languages.

6. domain-specific application of documents:

By developing a summarization system that is specialized for a particular domain (medical, legal, technical, etc.), it will be possible to provide summaries that reflect expertise in that field.

7. Realization of real-time summarization:

To provide real-time information summaries, a system could be developed to quickly extract and summarize information from news, social media, and other information sources.

8. Consideration of privacy and security:

It is important to design the summarization algorithm to avoid extracting information from documents that contain sensitive information, taking privacy and security into consideration.

9. collection of user feedback:

A cycle of collecting user feedback and incorporating it into improvements to the summaries will help improve the quality of the summaries.

abstract-typeset summary

<Overview>

Machine-learning abstractive summarization (Abstractive Summarization) will be a natural language processing task that generates new summary sentences instead of using sentences extracted from the original text document or sentence as is. Abstractive summarization is more advanced than extractive summarization, and the generated summary sentences can have new expressions beyond the combination of words from the original document. Below we discuss some points to consider in machine learning abstractive summarization.

1. data-driven approach:

Abstractive summarization takes a data-driven approach. It uses neural network models (in particular, recurrent neural networks (RNNs), transformers, or derivatives thereof) learned from a large corpus to learn the transformation of sentences into summary sentences. (For more information on neural network approaches, see e.g. “python Keras Overview and Examples of Application to Basic Deep Learning Tasks“)

2. training data:

To train an abstractive summarization model, we need a dataset of original documents and their corresponding summary sentences. This dataset may include manually generated or automatically generated summary sentences. 3.

3. evaluation and quality:

The quality of abstract summaries is evaluated by the naturalness, informational accuracy, and fluency of the generated summary sentences. Metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation) are used to help measure the quality of the summary.

4. uses:

Abstract summarization has been applied to a variety of natural language processing tasks, including summary article generation, machine translation, question answering, and document summarization. It is also used to compress and summarize information.

While abstractive summarization can generate more sophisticated summaries than extractive summarization, it can be difficult to train and evaluate models, and it is important to obtain appropriate training data and tune models.

<Algorithm used for abstractive summarization>

Abstractive summarization uses a variety of approaches and models to generate summary statements using machine learning algorithms. The following are typical algorithms and models used in abstract summarization.

1. sequence-to-sequence (Seq2Seq) model:

The Seq2Seq model is a neural network model consisting of an encoder and a decoder, first introduced for machine translation. The encoder encodes the input sentence into a vector representation, and the decoder generates a summary sentence from the vector. This model has also been applied to abstractive summarization to generate sentences; see “Autoencoder” for a description of one of Seq2Seq’s models, the Autoencoder.

2. transformer model:

The transformer model, described in “Overview of Automatic Sentence Generation with Huggingface” has revolutionized the natural language processing task, especially in BERT (Bidirectional Encoder Representations from Transformers ) and GPT (Generative Pre-trained Transformer), which can be applied to abstractive summarization to capture richer context.

3. attention mechanisms:

The attention mechanism, also discussed in “Attention in Deep Learning” is one of the core elements of the Seq2Seq and Transformer models, and is an algorithm that takes context into account by assigning different weights to each word in a sentence. This makes summary sentences more natural and semantic.

4. Pointer-Generator Network:

The Pointer-Generator network, described in “Overview of Pointer-Generator Networks and Examples of Algorithms and Implementations” is an extension of the Seq2Seq model that generates a summary by combining partial copies from the original document with the generated text. This approach emphasizes the accuracy of the information. This approach is useful when accuracy of information is important.

5. Transformer-based Causal Language Model:

The transformer-based causal language model, also described in “Overview of the Transformer-based Causal Language Model with Algorithm and Example Implementation” is a model that has the ability to generate the next word in a document given a portion of the document. This makes it possible to summarize an entire document.

<Example Implementation of Abstract Summary>

To implement abstractive summarization, a machine learning model must be used to generate summary sentences. The following is a simple example of abstractive summarization using Python and PyTorch. In this example, the Seq2Seq model is used to generate summary sentences.

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# Data Preparation (Toy Data)
input_texts = ["This is an example sentence.", "Another example sentence."]
target_texts = ["This is a summary.", "Another summary."]

# tokenize
input_tokens = [text.split() for text in input_texts]
target_tokens = [text.split() for text in target_texts]

# Vocabulary building
input_vocab = set(np.concatenate(input_tokens))
target_vocab = set(np.concatenate(target_tokens))
vocab = input_vocab.union(target_vocab)
vocab_size = len(vocab)

# Mapping vocabularies to the index
word_to_idx = {word: idx for idx, word in enumerate(vocab)}
idx_to_word = {idx: word for idx, word in enumerate(vocab)}

# Convert data to numbers
input_sequences = [[word_to_idx[word] for word in text] for text in input_tokens]
target_sequences = [[word_to_idx[word] for word in text] for text in target_tokens]

# Model Definition (Seq2Seq)
class Seq2Seq(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(Seq2Seq, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.encoder = nn.LSTM(embedding_dim, hidden_dim)
        self.decoder = nn.LSTM(embedding_dim, hidden_dim)
        self.output_layer = nn.Linear(hidden_dim, vocab_size)

    def forward(self, input_sequence, target_sequence):
        embedded_input = self.embedding(input_sequence)
        encoder_output, encoder_hidden = self.encoder(embedded_input)

        embedded_target = self.embedding(target_sequence)
        decoder_output, decoder_hidden = self.decoder(embedded_target, encoder_hidden)

        output = self.output_layer(decoder_output)
        return output

# Model Instantiation
embedding_dim = 128
hidden_dim = 256
model = Seq2Seq(vocab_size, embedding_dim, hidden_dim)

# Definition of loss functions and optimization algorithms
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# training loop
num_epochs = 100
for epoch in range(num_epochs):
    optimizer.zero_grad()
    input_sequence = torch.tensor(input_sequences, dtype=torch.long)
    target_sequence = torch.tensor(target_sequences, dtype=torch.long)

    output = model(input_sequence, target_sequence)
    output_dim = output.shape[-1]
    
    output = output.view(-1, output_dim)
    target_sequence = target_sequence.view(-1)

    loss = criterion(output, target_sequence)
    loss.backward()
    optimizer.step()

    if (epoch + 1) % 10 == 0:
        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')

# Reasoning (generation of summary sentences)
def generate_summary(input_text):
    input_tokens = input_text.split()
    input_sequence = torch.tensor([word_to_idx[word] for word in input_tokens], dtype=torch.long).unsqueeze(0)
    decoder_hidden = model.encoder(input_sequence)
    
    output_tokens = []
    for _ in range(max_output_length):
        decoder_input = torch.tensor(word_to_idx[""], dtype=torch.long).unsqueeze(0)
        decoder_output, decoder_hidden = model.decoder(decoder_input, decoder_hidden)
        predicted_word_idx = torch.argmax(decoder_output, dim=2).item()
        predicted_word = idx_to_word[predicted_word_idx]
        output_tokens.append(predicted_word)

        if predicted_word == "":
            break
    
    summary = " ".join(output_tokens).replace("", "").replace("", "")
    return summary

# Summary Sentence Generation
input_text = "This is an example sentence."
max_output_length = 10
generated_summary = generate_summary(input_text)
print("Generated Summary:", generated_summary)

This code example is very simple, and many improvements are needed for the actual abstractive summarization task, and factors such as training data, hyperparameter tuning, and evaluation are also important.

<Abstraction-Type Summarization Tasks>

There are several challenges in abstract summarization that must be overcome when working to improve its quality. The following are some of the major challenges of abstract summarization

1. natural sentence generation:

Abstractive summarization requires the free combination of words and phrases to generate summary sentences. However, there are challenges to generating natural sentences, which often result in unnatural or grammatical errors in the generated sentences.

2. content accuracy:

In the automatic generation of summary text, it is sometimes difficult to accurately capture the content of the original document. The model may misinterpret information or generate incorrect information, resulting in inaccurate information.

3. reduction of redundancy:

Abstract summaries may also contain the same information repeatedly or use redundant expressions. Reduction of redundancy is one of the factors that contribute to improved summary quality.

4. appropriate summary length:

The length of the summary text should be adjusted according to the type of document and its requirements. If the summary is too short, important information may be missing; if too long, it may be redundant.

5. dealing with unknown information:

Abstract summaries are challenged by how to handle new topics and information that did not exist in the training data. Since the model generates summaries based on known information, it is limited in its ability to deal with unknown information.

6. lack of training data:

Training a high-quality abstractive summarization model requires a large and diverse set of training data. However, data on specialized domains and languages may be lacking.

7. difficulty of evaluation:

Automatic evaluation metrics can have difficulty accurately measuring summary quality; metrics such as ROUGE and BLEU are commonly used, but may require evaluation by human evaluators.

Various methods are being considered to overcome these challenges. They are described below.

<Measures to Address the Challenges of Abstraction-Type Summarization>

The following measures can be considered to address the challenges of abstraction-based summarization:

1. improve natural sentence generation:

Natural sentence generation can be aided by the use of more sophisticated language models. Recent transformer-based models (e.g., GPT-3, GPT-4, T5) are excellent at producing natural sentences and can be leveraged.

2. content accuracy:

To improve content accuracy, it is important to improve the quality of training data and ensure that it does not contain inaccurate information. Fact-checking in advance is also a means of ensuring the accuracy of information.

3. reducing redundancies:

To reduce redundancy, it is effective to implement algorithms and heuristics that automatically detect and remove duplicate information in the generated text. It will also be important to measure the quality of the generated sentences and consider automatically filtering out redundant sentences.

4. appropriate summary sentence length:

A useful approach to adjusting the length of summary sentences is to employ methods that limit the number of tokens or characters in the generated text. It will also be important to provide parameters that allow the length to be customized based on document type and requirements.

5. handling of unknown information:

To deal with unknown information, a useful approach would be to use a large, versatile model, such as a transformer model, that can cover many topics and domains. It will also be important to adjust the model by adding topic-specific data.

6. lack of training data:

To address the lack of training data, a good approach would be to collect data on specialized domains and languages to expand the training data set. It is also important to consider learning methods other than data augmentation and supervised learning.

7. evaluation difficulties:

To address the difficulty of evaluation, an effective approach would be to use automatic evaluation metrics (ROUGE, BLEU, etc.) as well as evaluation by human evaluators. It will also be important to work on the development and improvement of new evaluation metrics.

Reference Information and Reference Books

For more information on natural language processing in general, see “Natural Language Processing Technology” and “Overview of Natural Language Processing and Examples of Various Implementations.

Reference books include “Natural language processing (NLP): Unleashing the Power of Human Communication through Machine Intelligence“.

“Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems“

“Natural Language Processing With Transformers: Building Language Applications With Hugging Face“