Text summarization is so prominent in natural language processing (NLP) that it made our top ten list of NLP techniques to know.
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Natural Language Processing (NLP) text summarization has a sizable impact on people’s lives. Due to time constraints, reading an article carefully in the digital age is no longer an option. Keeping track of the expanding number of articles on the web has become very challenging due to the increased production and the digitization of articles in printed media. As a result, text summarization is a necessary tool that helps condense long texts.
An everyday use case of text summarization is Google Search. It is used by the average person more than three times per day. You get better results for your search queries because of knowledge panels or featured snippets. In response to user queries, Google uses featured snippets to show an article’s summary. These passages were lifted from online sources and summarized for the end user.
Search engines must make sense of all the articles to retrieve the correct ones.
What is NLP text summarization?
Extracting concise summaries from massive amounts of text using natural language processing (NLP) is known as “text summarization.” The summary should be written in clear, succinct language that makes sense to the reader. According to Statista, more than 180 zettabytes of data will have been produced, stored, copied, and used globally by 2025. To utilize and analyze this text data more conveniently, most of it needs to be reduced to clearer, shorter summaries that include the essential information.
Machine learning algorithms that can quickly summarize lengthy texts and provide precise insights are in great demand. Text summarization is very useful in situations like these!
Text classification, legal text summaries, news summaries, creating headlines, and other NLP tasks benefit from text summarization.
Types of NLP Text Summarization
The two main methods for summarising text are extractive and abstraction.
Key phrases are taken from the original text and combined to form the summary using the extractive text summarisation method. Using a scoring system, extractive summarization picks out only the phrases that are most pertinent to the meaning of the source text. This method extracts the necessary text while maintaining the documents’ integrity according to the set criteria. The extractive summarisation technique uses the LexRank, Luhn, LSA, and other algorithms implemented using the Python libraries Gensim or Sumy.
To create a new set of sentences for the summary, abstractive summarization concentrates on the most critical information in the original text. The new phrase isn’t found in the original text. This strategy completely differs from extractive text summarization, creating a summary based on the original text. This method entails locating crucial components, interpreting the context, and reinventing them. It promises to convey the essential information in the fewest possible words. The abstractive summarization method is compatible with well-known Python frameworks and packages (Spacy, NLTK, etc.) and deep learning models like the seq2seq model and LSTM, among others (Tensorflow, Keras).
What are the top machine learning algorithms for NLP text summarization?
This section summarises the most prominent text summarization techniques used in machine learning.
1. PageRank Algorithm
PageRank is a Google Search algorithm that ranks websites in search engine result pages. It is named after Larry Page, one of Google’s founders. Google employs a variety of algorithms, but PageRank is the first and best-known algorithm the company has ever used. The importance of a website’s pages can be assessed using PageRank. By monitoring the quantity and calibre of links pointing to a page, PageRank develops an approximation of the significance of that page. The underlying premise is that websites with greater authority are more likely to receive links from other websites.
When a user clicks on a link, the PageRank algorithm creates a probability distribution that shows the likelihood that they will land on a particular page. Practically any extensive collection of documents can use PageRank. Numerous research articles imply that the distribution is equally distributed among all data sets at the beginning of the computational process. Multiple iterations through the collection are required for PageRank calculations to accurately modify estimated PageRank values to represent the actual potential value.
2. TextRank Algorithm
An unsupervised extractive text summarization method called TextRank is comparable to Google’s PageRank algorithm. It aids in phrase ranking, automatic text summarization, and keyword extraction. In many ways, the TextRank algorithm and PageRank algorithm are similar.
Unlike PageRank, which works with web pages, TextRank uses sentences. The PageRank algorithm determines the likelihood of a web page transition, whereas the TextRank algorithm compares the similarity of any two sentences. The matrix used for the PageRank approach is the same square matrix that the TextRank approach uses to store the similarity scores.
3. SumBasic Algorithm
A method for summarising multiple documents called SumBasic determines the frequency distribution of words in all documents. To produce a precise and accurate summary, the algorithm prioritizes frequently occurring words in a document over less frequently occurring words. Following the word pattern of each sentence, it calculates the average probability of each sentence. It then chooses the highest-ranking sentence that contains the most frequent word until the desired summary length is reached.
What are the top NLP text summarization tools in Python?
The Natural Language Toolkit (NLTK) is a popular NLP python library with many common NLP algorithms. To use it for text summarization, you can tokenise the sentences and then use the popular tf-idf algorithms to assign weights to the sentences. The highest-rated sentences can then be used as a summary.
Gensim is a Python package that relies on NumPy and SciPy. It is memory independent, so doesn’t need to store the whole training data set in RAM, making it ideal for larger data sets. Gensim has Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) implementations as well as the ability to create similarity queries. For its text summarization method, it uses the TextRank algorithm described above.
Sumy is a Python package that automatically summarises text documents and HTML pages. It uses LEX-RANK and LUHN.
LEX-RANK uses a graph-based phrase centrality score as the foundation for its unsupervised text summarising approach. It uses the strategy of seeking similar sentences and marks these as extremely significant.
LUHN bases his heuristic text summarization on the recurrence of the most crucial phrases.
Spacy is another popular Python package that has CNN models for part-of-speech tagging, dependency parsing, text categorization, and named entity recognition that can be combined to build a text summarization method.
What NLP text summarization tools use Deep Learning models?
The most widely used deep learning models for abstractive text summarization are recurrent neural networks (RNNs), convolutional neural networks (CNNs), and sequence-to-sequence models. The sequence-to-sequence model, the attention mechanism, and transformers (BERT) are introduced in this section.
1. Sequence-to-Sequence Model (Seq2Seq Model)
A series of sentences are used as input, and an additional sentence is produced as output by the Seq2Seq framework. Neural machine translation uses a single language as the input, translating it into translated sentences in the output language. Encoder and decoder are the two main strategies used in seq2seq modelling.
2. Encoder Model
It’s common practice to encode or modify the input phrases while providing feedback at each stage using the encoder model. This feedback can be an internal state, such as a hidden state or a cell state if you employ the LSTM layer. Encoder models take essential data from input texts while preserving the context. Then the encoder model in Neural Machine Translation will receive your input language and record contextual information without affecting the rest of the input sequence. Finally, the output sequences are obtained by feeding the outputs of the encoder model into the decoder model.
3. Decoder Model
The decoder model decodes target texts word-by-word or predicts them. Target sentence input is accepted by the decoder input data, which predicts the following word and sends it to the prediction layer below. The model uses the words “start>” (the beginning of the target sentence) and “end>” (the ending of the target sentence) to decide what will be the initial variable for predicting the next word and what will be the finishing variable for determining the end of the sentence. During training, you give the model the word “start>,” and it then predicts the next word—the decoder target data. The following step indicates the word based on this word, which is then used as input data.
4. Attention Mechanism
The attention mechanism was initially developed for neural machine translation before being applied to NLP tasks like text summarization. Long sentences may be difficult to provide using a simple encoder-decoder architecture because it cannot evaluate long input parts. The attention mechanism aids the retention of the information, significantly affecting the summary. At each output word, the attention mechanism assigns a weight between the output word and each input word; the weights add up to one. The benefit of using weights is that they can identify which input word needs extra care when it comes to the output word. The mean value of the decoder’s final hidden layers is calculated after each input word has been processed. It is given to the softmax layer and the last hidden layers in the current phase. Two categories of attention mechanisms exist.
Global Attention: It generates the context vector using the encoder model’s hidden states from each time step.
Local Attention: It generates the context vector using some hidden states from the encoder model in local attention.
5. Transformers – BERT Model
A multilayer bidirectional transformer encoder is used in the BERT (Bidirectional Encoder Representations from Transformers) word embedding technique. Instead of sequential recurrence, the transformer neural network employs parallel attention layers. The BERT system creates a single, massive transformer by combining the representations of the words or phrases. Additionally, BERT uses an unsupervised method for pre-training on a sizable amount of text. In the BERT model, two tokens are added to the text. The entire text sequence information is combined in the initial token (CLS). Each sentence ends with the second token, which is (SEP). The final text is composed of several tokens, and each token has one of the three types of embeddings: segmentation, position, and token.
BERT is a popular transformer.
BERT is an extremely popular NLP text summarization technique. This is why:
- Its main advantage is that it has been trained on 2.5 billion words and uses bi-directional learning to simultaneously obtain the context of words from both left-to-right and right-to-left contexts.
- Next Sentence Prediction (NSP) training enables the model to comprehend how sentences relate to one another during the model training process. The model can now gather more data as a result.
- The BERT model can be used on smaller datasets and still produce good results because it was effectively pre-trained on a massive amount of data (English Wikipedia- 2500 words).
NLP text summarization key takeaways
- NLP text summarization is extremely popular and has many really useful use cases.
- The most notable machine learning algorithms used for summarization are PageRank, TextRank and SumBasic.
- There are many great libraries to choose from in python. NLTK, Gensim, Sumy and Spacy all allow you to implement text summarization differently.
- Regarding deep learning models, BERT is by far the most popular option for text summarization.
- Read this article for other popular deep learning models for natural language processing.
At Spot Intelligence, we also love using summarization techniques, as information overload is a problem frequently encountered. We, therefore, use all of the techniques and tools mentioned here. What do you use for your projects and why? Let us know in the comments.