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Using NLP Text Summarization in Your Business

James Briggs discusses NLP Text summarization and how businesses can easily implement it and benefit from it.

4 mins readJune 07, 2021

"With a few codes, you can easily command your computer to start working with several advanced machine learning algorithms on major complex natural language problems in your business."

Online magazines, research sites, and news aggregator apps rely on short, informative summaries to keep readers interested and up to date. For example, when you scroll through the news on your favorite website, you can generally scan a series of headlines and short descriptions. This makes it easier to find the articles that interest you. Summaries are a helpful tool for getting the information you want quickly. The extensive generation of data and content on the internet has given rise to a need for text summarization to generate efficiency and reduce reading time.

Text summarization is the process used in extracting short and informative summaries from the vast original text without losing any vital information during the process. The method of automated text summarization involves summarizing significant texts into more minor texts that you can consume quickly. The resulting summary must be continuous, fluent, and depict the significance.

There are two methods of text summarization that use natural language processing (NLP): extractive and abstract.

  • Extractive approach of text summarization: The extractive method uses an NLP summarizer that involves identifying the significant sentences in the original text and adding them into the short article that forms the summary. Extractive is the traditional method. The sentences picked from the original document or paper are chosen based on a scoring function. The summary obtained when using this method contains the original sentences from the original article. Hence, the extractive approach works by identifying vital sections of the text, cropping the units out, and stitching them together to create a condensed summary.

  • Abstractive method of text summarization: Abstractive is more advanced than the extractive process. In this method, an abstractive NLP summarizer identifies the critical sections in the original article, interprets the context of the text, and rewrites new sentences to create a summary. The original text is interpreted using the latest natural language techniques and new precise sentences that convey the original information. This method aims to ensure that the article's core information is conveyed through the shortest possible texts. Sentences generated by the abstractive method are new and not extracted from original texts. The advantage attached to using the abstractive method is that the core information is still conveyed in a linguistically fluent manner. However, the software used presents a challenge. The language used in the abstractive approach is generally more complex and can be difficult for the user.

Applying automatic text summarization to significant data is much easier when you use NLP technology. For businesses, this means more efficient means of summarizing legal texts, performing text classification, answering questions, summarizing news, and generating headlines.

How can Businesses Benefit from Automatic Text Summarization for Business?

Automatic text summarization for business uses two leading technologies: natural language processing and natural language generation (NLG). The NLP technology allows computers to understand human language. NLP technology enables computers to read, edit and summarize texts. The NLP technology then flows into the NLG technology, which enables computers to create their content.

For example, Siri and Alexa can help you to understand automatic text summarization for business better. Siri uses NLP technology to understand requests and NLG technology to respond in natural-sounding language.

Whether written or spoken, there is a lot of information wrapped up in human language. Text summarization use cases with NLP and NLG technologies is a game-changer to the industry. Automatic text summarization for business can extract essential information from thousands of pages of documents and summarize them into short articles. NLP summarizer tools include ML Analyzer and MeaningCloud.

Some of the benefits a business can get from using an NLP summarizer include the following:

  • Automatic text summarization saves time. To summarize a text, you must read it entirely, develop an understanding and generate a summary. Reading through an entire raw article, dissecting it, and segregating the main ideas is time-consuming and requires a lot of effort. A person can take up to 15 minutes to read an article comprising 500 words. Automatic text summarization software does the same work of reading, dissecting, and summarization in a split second. Since the introduction of text summarization by NLP technology, users read less data and still receive the targeted information within a short time.

  • An NLP summarizer can save your business money. Summarization tools reduce the need to find expertise outside the company. For instance, to summarize an original text into a foreign language, a business would need to hire someone with bilingual skills to conduct the task. Text summarization software works in any language and can summarize texts into most languages without human intervention, thus eliminating the cost of a translator.

  • NLP summarizer software allows businesses to focus on core tasks. The software enables automatic summarization of not only documents but also web pages. Employees can focus their attention on their specific department tasks while summarization software derives important information from original texts and summarizes it into specific articles for categories on the website. The business can enjoy better productivity alongside the summarization process.

  • Automatic text summarization generates high-quality summaries regardless of the size of the business. Depending on the size and nature of your business, the number of documents you need summarized may vary. Fortunately, NLP summary software can range in customized volume output while still producing verified, high-quality materials for your website or other documentation needs.

How can a Company Implement Automatic Text Summarization for Business?

Companies can implement the NLP summarization in three major steps.

  1. Import and initialization
  2. Data and tokenization
  3. Summary generation
  • For the initialization of your summary, it would be best if you imported PyTorch, AutoTokenizer, and AutoModelWithLMHead. Imports and initialization involve the fundamental setup of a clean and functional environment for text summarization. The first step is the importation and installation of Python. There are many options for importing and initializing Python. However, for a simple process that maintains a fine level of control, you can use Anaconda. Anaconda gives you an environment that is easily manageable with tools such as Spyder and Jupyter. After installation, get the latest version of Python and ensure that everything is set up correctly. You should also ensure that the model and the tokenizer are correctly initialized.

  • The next step involves getting the needed data and tokenizing it. Tokenization of data is the process involved in turning real data into a string of characters called tokens that relate to the original content. That relationship is stored in a token vault. In the case of summarizing a document, the tokenizer takes every word and punctuation character from the original text. It converts those into numeric code, which is then read and mapped to a pre-trained word embedding by the T5 encoder-decoder model. Using the T5 model, call the tokenizer, and encode it on your input data. The output data is the tokens.

  • The final step is summarizing your tokenized data using the T5 model. Summary generation involves producing a summary based on the topmost important sentences. Summary generation substitutes words with their weighted token frequencies and computes the sum. From the sum calculated, you can deduce the sentence with the most weight in the article and use it as the best summary representative. The aggregate gives the importance of the sentences in order. From the sum, an average score is calculated. The average score acts as a threshold that will help you avoid the selection of a sentence with a lower score than the average score, giving you a high-quality result.

An NLP Summarizer for Your Business

The introduction of business summarization using NLP technology has eased the summary generation process. With a few codes, you can easily command your computer to start working with several advanced machine learning algorithms on major complex natural language problems in your business.

Check out the video tutorial in here!

Author’s Bio:

James Briggs - James works as a data scientist specializing in the field of natural language processing (NLP). Alongside his role as a data scientist he also mentors others through his online blog, videos, and private tutoring. You can follow James on Youtubeand Twitter.





Author’s Bio:

Timothy Carter

James Briggs discusses NLP Text summarization and how businesses can easily implement it and benefit from it.