What is summarizing?
As the volume of written information continues to grow exponentially, the ability to efficiently and accurately condense and comprehend lengthy blocks of text has become more valuable than ever before. Text summarization, a subfield of natural language processing (NLP), offers a solution to this problem through advanced algorithms and techniques that analyze and extract the most essential information from a given text.
Not only does text summarization save time and effort, but it is also crucial in ensuring the accuracy and completeness of a summary, especially when dealing with technical documents or sensitive legal and financial materials. The benefits of text summarization extend to a wide range of fields and professions, making it a highly useful tool for anyone looking to stay informed and stay ahead in today's information-saturated world.
In this article, we'll take a closer look at what NLP text summarization is, how it works, its benefits, and some common use cases. So, keep reading to learn more about this powerful tool.
How does it work?
Text summarization involves the use of algorithms and techniques to analyze and extract the most important information from a given text. Here are three common types of summarization:
- Input-based summarization
- Output-based summarization
- Purpose-based summarization
The picture below represents the interrelations between the types of summarization:
Let’s delve deeper into each type of text summarization below.
One common way to classify text summarization is based on the input, or source of the text being summarized. For example, we can classify summarization as single-document summarization, where the input is a single document, or multi-document summarization, where the input is a collection of multiple documents. Multi-document summarization can be more challenging than single-document summarization, as it requires the ability to understand the relationships between multiple texts and to synthesize information from multiple sources.
Another way to classify text summarization is based on the output, or format of the summary. There are two main types of output-based summarization: extractive summarization and abstractive summarization.
Extractive summarization involves selecting and condensing key information from the original text while maintaining the overall meaning and structure of the original content. This type of summarization typically involves identifying the most important sentences or phrases in the original text and including them in the summary.
On the other hand, abstractive summarization involves generating a summary that is a new and unique representation of the original text, rather than simply extracting information from it. This type of summarization typically involves using techniques from natural language processing (NLP) and machine learning to understand the content and context of the original text, and then generating a summary that captures the essence of the text. Abstractive summarization can be more challenging to automate, as it requires a deeper understanding of the content and the ability to generate new text that captures the meaning and structure of the original text.
Based on the purpose
Purpose-based summarization is a type of text summarization that is based on the goal or purpose of the summary. There are two main types of purpose-based summarization: informative summarization and argumentative summarization.
Informative summarization is a type of text summarization that aims to provide an overview of the main points or ideas in a text. It involves condensing a long document or article into a shorter, more digestible summary, or extracting key points or highlights from a text.
Argumentative summarization, on the other hand, is a type of text summarization that involves evaluating and critiquing the arguments presented in a text. It is often used to assess the strengths and weaknesses of an argument or to identify the key points of contention in a debate.
Overall, the best type of text summarization will depend on the specific needs and goals of the task at hand, as well as the availability of resources and expertise. It may be necessary to use a combination of approaches of summarization to achieve the desired results.
Benefits & Use cases
Text summarization is a powerful method that can be applied in many different fields and contexts. By distilling a longer piece of text into a shorter, more concise version that captures the essence of the original, text summarization can save time, improve comprehension, and enhance organization, learning, and communication. These benefits make text summarization an invaluable tool for anyone seeking to efficiently and effectively process, understand, and communicate information.
Here are some use cases of text summarization:
- Summarizing email threads: If you frequently receive long email threads with multiple responses from different people, text summarization could be used to quickly extract the key points and main takeaways from the conversation. This could be especially useful for busy professionals who need to quickly understand the content of an email thread without having to read through every single message.
- Summarizing product video meetings: If you hold regular video meetings with your team to discuss new product ideas or updates, text summarization could be used to quickly extract the key points and main takeaways from the meeting. This could be especially useful if you have team members who are unable to attend the meeting in person, as they could use the summary to quickly catch up on the main points that were discussed.
- Creating chapters for podcasts or YouTube videos: If you produce podcasts or YouTube videos, text summarization could be used to automatically generate chapters or sections for your content. This could make it easier for listeners or viewers to navigate through your content and find the specific sections they are interested in.
- Summarizing customer feedback or reviews for businesses that want to quickly identify common themes or issues. According to Forrester’s Q2 2020 State Of VoC And CX Measurement Programs Survey, 61% of respondents said their companies don’t have a formal process for closing the customer feedback loop. By quickly and accurately extracting the key points and main takeaways from customer feedback, companies can more effectively address the needs and concerns of their customers and improve the overall quality of their products and services.
- Summarizing legal documents or contracts for lawyers or other professionals who need to understand the main points of a document, but don't have the time to read through the entire document.
Popular text summarization applications
There are many text summarization tools available that can help you quickly and accurately extract key points and insights from large amounts of text. Some popular options include:
- GPT-3 Summarizer: Developed by OpenAI, this tool uses advanced language processing technology to generate human-like summaries of text.
- MonkeyLearn is a cloud-based text analytics solution that has a free text summarizer as well as pre-trained text analysis models like text classifiers and text extractors.
- SummarizeBot: This tool uses natural language processing and machine learning algorithms to generate summaries of text in multiple languages.
- One AI has an AI-Powered Summary Tool built with One AI’s Language Skills API. The summary API creates context-aware summarizations of texts, including news reports, articles, and conversations. The results are concise, contain all the relevant information, and can be used in conjunction with other Language Skills to improve results by processing only the key information.
Text summarization has revolutionized the way companies process and understand written information. It has enabled them to efficiently extract key points and insights from large amounts of text, saving time and resources. The potential for text summarization to improve and grow is extensive, as it can significantly improve our ability to stay informed and make better decisions. However, there are challenges to overcome, such as the need for high-quality training data and the potential for biased or incomplete summaries.
Fortunately, One AI, as an AI-powered platform, can help address these challenges and provide accurate and comprehensive text summaries for a wide range of cases. Check out our automatic text summarizer tool or go straight to our Language Studio to see the vast possibilities it can bring to your business.