It can take a person days, or even weeks, to sift through a 50-page technical document, filter out irrelevant material, and write a complete summary of the text without compromising on correctness.
When you consider sensitive legal and financial documents, there is no room for error including leaving an important detail out.
This is where data science, AI, machine learning, and natural language processing (NLP) text summarization comes in handy.
Read this article to learn what NLP text summarization is, how it works, its benefits, and use cases.
What is Text Summarization in NLP
NLP text summarization is the process of breaking down lengthy text into digestible paragraphs or sentences. This method extracts vital information while also preserving the meaning of the text. This reduces the time required for grasping lengthy pieces such as articles without losing vital information.
Text summarization is the process of creating a concise, coherent, and fluent summary of a longer text document, which involves underlining the document’s key points.
There are two approaches to text summarization.
- Extractive approaches
- Abstractive approaches
Text summarization uses AI and NLP to take large amounts of data and extract the most relevant details from the text.
The most important points within the document are summarized, while the meaning of the data is maintained. With the volume of data being constantly generated, research analysts do not have the time to read through every word and extract important points manually.
Two Approaches to NLP Text Summarization
Extractive Text Summarization Approaches
With these approaches, we use an extractive technique to summarize our material using algorithms.
For example, if we want to summarize our text using the frequency technique, we store all of the key terms as well as the frequency of all of those words in a dictionary.
We record the sentences containing those terms in our final summary based on high-frequency words.
This ensures that the words in our summary come from the text.
The extraction is carried out in accordance with the specified measure, with no changes made to the text. See the graphic below for a description of the extractive summarization process.
To sum it up, this method detects essential pieces of text, cuts them out, and then stitches them back together to generate a condensed or concentrated form.
Abstractive Text Summarization Approaches
Abstractive summarizers do not produce the summary by selecting sentences from the original text passage; instead, they create a paraphrase of the key points of a given text, using a set of words that differs from the original.
To recap, this is quite similar to what we do as humans. In our minds, we form a phonological representation of a document.
We then select terms from our general vocabulary (words we frequently use) that fit in the semantics to construct a brief summary that conveys all of the document’s meaning.
As you can see, constructing this type of summarization may be tough because it would require Natural Language Generation.
In contrast to the extraction approach, where we only use the terms that are present – the sentence should not end here, it’s missing the main point of the sentence! (In contrast to … this method does this and that) . It is highly likely that the abstractive summary phrases do not exist in the original text.
Below are the processes for extractive and abstractive summarization.
Six Benefits of NLP Text Summarization
1. Scalable and Quick
Manually summarizing a short document is fairly easy, but what if you have an article or paper that is hundreds or thousands of pages long?
Rather than having dozens of employees manually going through thousands of documents, you can automate this analysis with the Accern NoCodeNLP Platform. Our software will analyze all your input text and source documents and provide you with a summary text.
2. Leverages Existing Tools
To take advantage of text summarization, you don’t have to build machine learning infrastructure in-house or hire a data science team. The technology exists and it’s accessible to everyone.
Most CEOs or directors may get confused just hearing the buzzwords: artificial intelligence, machine learning models, NLP tasks, textrank algorithm, etc.
But there’s absolutely no reason for feeling discouraged when No-Code NLP platforms are available.
The summarization of documents and transformation of data, words, and sentences into decisions is possible and already used in a variety of industries with AI / ML / NLP platforms like ours.
My point here is, you do not have to fully understand how text summarization works or be an expert. We can supply you with the technology that is able to effortlessly extract key points from a given document and provide a detailed summary using NLP.
Our text summarization algorithms are easy to use and available to make your research and business decision-making process more efficient and actionable.
3. Understand Your Customers Better
You can never go wrong with having a better understanding of your customers.
The data on your customers may come in many forms, such as spreadsheets, chat messages, emails, etc. And of course it might come in different languages too.
In customer relationship management, text summarization refers to compressing all the above-mentioned customer data, and turning it into abridged summaries that you can present in a meeting or use in other business processes.
Since NLP can extract insight from text data, this makes it a perfect tool for keeping track of customer feedback, determining sentiment, whether it’s positive or negative, and to what degree.
This allows organizations to monitor reviews in real-time and flag the most important or time-sensitive comments, provide timely feedback, and ignore irrelevant information.
4. Summarize the Text in Different Formats
Whether you are conducting research or launching a new product, one of the first steps is analyzing your market and competitors.
How do you do that given the volume and velocity of information and remain productive?
Natural language processing helps you obtain summarized text extracted from your competitor’s web pages, market research documents, industry-related articles, etc. Having a clear idea of the market and your competitors helps you determine actionable steps for presenting your product or refining your business strategy. This helps you stand out amongst the competitors and maintain a competitive advantage in the market.
The Accern NLP platform can provide you with the most relevant sentences that you can use to communicate your product, important points to focus on and give you a deep understanding of your environment.
People who are not familiar with extracting data from financial statistics or reports, for example, can use automated text summarization for capturing a synopsis of those statistics and reports.
5. Ensure all Critical Information is Covered
The human eye can overlook important details, while standalone software is more accurate. What every reader needs is the ability to extract what is useful to them from any piece of material.
The automated text summarizing approach makes it easy for the user to read all the most important sentences in a document.
6. Run Sentiment Analysis
Sentiment analysis uses artificial intelligence and natural language processing to identify, extract, and analyze textual data to understand the overall attitude and emotional tone of a text.
Once the data is evaluated, a sentiment score will be generated to determine whether the data is positive, negative, or neutral and to what extent.
Sentiment analysis is used across many industries, including by hedge funds for analyzing financial news to predict stock market trends and movements. It is also used by traders and investment bankers to research, extract data on and analyze ESG compliance and mergers and acquisitions news.
With sentiment analysis (downloadable white paper), financial teams can even evaluate consumer sentiments around specific companies.
NLP Text Summarization Use Cases
Use Case: Financial Research with NLP
Financial and investment decisions require an in-depth investigation and classification of a significant quantity of information. This is true for both individual investors and investment firms. This is where an automatic text summarization designed for evaluating and condensing financial information can come in handy.
People who are unfamiliar with extracting data from financial statistics or reports, for example, can use automated text summarization for providing a concise summary of those financial reports.
Watch this 3-min video to see how asset managers can leverage the Accern NoCodeNLP Platform to receive fast and accurate insights from text data to fuel timely investment decisions.
Use Case: Media Monitoring with NLP
Assume you need to learn about the current state of an industry from a variety of publications and media. But you hardly have time to scan all the headlines, let alone read all of them and get to the meat of their arguments.
In this circumstance, text summarization can help you scan more information by extracting the summary of numerous news articles and other media.
Having the exact sentences that can influence your decision-making is possible through NLP text summarization. You, or your team, does not have to go through tons of text data manually, in an attempt to spot and remember all the significant sentences that you have read. Innovations like the Accern NoCodeNLP Platform make it easier now to stay focused on key insights no matter how large your information sources may be.
Staying focused and up to date regardless of the velocity and volume of information is now easier than ever thanks to NLP technology.
Schedule a demo to learn more about the Accern NoCodeNLP Platform and how it can help drive fast and accurate decision making with NLP text summarization.Share this Post!