Text summarization helps generate a summary of customer feedback or reviews about different products. It's not solely focused on customer service but helps in that department.
By using natural language processing (NLP) techniques, businesses can quickly identify the most common issues and topics that customers raise in their feedback and reviews.
This information can then enhance the quality of products and services, improving customer satisfaction and brand loyalty in the long run.
Text summarization is a highly effective tool that offers valuable insights to businesses regarding their customers' needs and preferences.
By analyzing massive amounts of data and filtering the most important sentences, businesses can better comprehend their customers' current and future requirements and develop new products that cater to them.
This approach ultimately results in increased revenue and greater profitability for businesses. In this post, we will discuss the types of text summarization and the benefits it offers.
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What is text summarization?
Text summarization is extracting a short and informative summary from large text pieces by only showing the most important information.
This automated method condenses large amounts of text into smaller ones that can be consumed quickly.
The resulting summary should be coherent and compelling, conveying the main ideas fluently.
Two methods of text summarization utilize NLP: extractive and abstractive.
Extractive text summarization
The traditional method of summarizing text is called extractive text summarization. This involves using an NLP summarizer to identify essential sentences from the original text and adding them into a short article that forms the summary.
How extractive text summarization works
The sentences chosen from the summary length of the original document or paper are based on a scoring function.
The resulting summary contains the original sentences from the summary produced by the original article. In essence, the extractive approach involves:
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identifying key sections of the text,
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selecting them,
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stitching them together to create a condensed summary.
Abstractive text summarization
Abstractive summarization is more advanced than the extractive method of summarizing text.
Instead of simply selecting and compiling essential sentences from the original text, an abstractive NLP summarizer identifies the critical sections.
How the abstractive text summarization works to make a summary
It interprets the context of the text to create a new summary using new sentences. This method aims to convey the core information of the article in the shortest possible text.
Challenges of the abstractive method
The abstractive method generates new sentences not extracted from the original text, but the advantage is that it conveys the information linguistically fluently.
However, the software used for abstractive summarization can present a challenge because the language is generally more complex, making it difficult for some users to comprehend.
Using NLP technology makes applying automatic text summarization to big data easier.
This means businesses can more efficiently summarize legal texts, perform text classification, answer questions, summarize news, and generate headlines.
Natural language processing uses machine learning and more
NLP is a field of computer science that focuses on developing artificial intelligence (AI) systems that can understand human language.
NLP combines computational linguistics, statistical models, machine learning, and deep learning to make computers process written and spoken language.
By doing so, NLP allows computers to accurately interpret all the words, meanings, intentions, and emotions behind human language.
NLP is a technology that powers computer programs to perform tasks like translating text from one language to another, responding to spoken commands, and summarizing large volumes of text in real time.
You might have interacted with NLP through voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other similar technologies.
NLP is used in consumer applications and in enterprise solutions to simplify mission-critical business processes, streamline business operations, and increase employee productivity.
How can businesses benefit from automatic text summarization?
Automatic text summarization for business involves two leading technologies: natural language processing and natural language generation (NLG).
NLP allows computers to comprehend human language and read, edit, and summarize text.
This technology then moves into NLG, enabling computers to take text sequences and create content.
Examples of NLP and NLG working together
Siri and Alexa are good examples of how NLP and NLG technologies work together. Siri uses NLP to understand user requests and NLG to respond in a natural-sounding language.
Human language, whether written or spoken, contains vast information. The use of NLP and NLG technologies in text summarization is a game-changer for the industry.
Automatic text summarization for businesses can extract vital information from thousands of pages of documents and present it in concise articles.
Let’s discuss some benefits a business can get from using an NLP text summarizer.
Saves time by finding the important sentences to create a summary
To create a well-written summary, you need to read through the entire article carefully, understand its main ideas, and then generate a summary.
However, this can be quite time-consuming and requires a lot of effort. It takes up to 15 minutes for a person to read a 500-word article, analyze it, and extract its main points.
With automatic text summarization software powered by NLP technology, this can be done in seconds.
This technology has allowed users to read less data while receiving essential information quickly.
Saves money
Summarization tools are helpful for businesses as they eliminate the need to hire an expert outside the company for text summarization tasks.
For example, if a company wants to write a summary of a text in a foreign language, it typically needs to hire a bilingual individual to do the job.
However, text summarization software can perform the task in any language without human intervention, so businesses can save money on hiring a translator.
Allows businesses to focus on core tasks
The software can automatically create a summary of documents and web pages. This means that employees can concentrate on their specific department tasks.
At the same time, the summarization software extracts essential information from the original texts and summarizes it into specific articles for various categories on the website.
By training data, the business can enhance its productivity alongside the extraction-based summarization process.
Produces a high-quality summary
The number of documents a business needs to summarize can vary depending on size and nature.
NLP summary software can produce high-quality and verified materials for your website or other documentation needs.
This software can be customized to produce multiple layers of output in different volumes, making it suitable for businesses of various sizes.
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Conclusion
Text summarization is essential to understanding mass amounts of information. It saves time and money and allows you to tackle problems quickly.
Business is competitive, and you need every advantage you can get to be successful.
Using technology, you can understand your customers and identify room for improvement with a concise summary.
This will let you stay ahead and beat the competition. Text summarization can be an efficient way to gain these valuable customer insights.
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