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With the large amounts of information circulating in our digital spaces, condensing text data while preserving its meaning efficiently is important. Text summarization, a subfield of machine learning and natural language processing (NLP), is the technology that allows this to happen. There are several different methods for text summarization, with new ones being developed daily. These are usually categorized as either extractive or abstractive.
What is Text Summarization?
A Natural Language Processing (NLP) activity, text summarizing, shortens a larger text document. It also involves creating a summary that retains the information and meaning of the original text. It is an important NLP tool as it allows you to access valuable data quickly and efficiently without spending a lot of time reading. This AI-powered text summarizer is useful for various applications, including customer relationship management (CRM), market research, and business operations. It can be used to monitor reviews, analyze feedback, and determine sentiment, all of which can help businesses make better decisions. It is possible because NLP can extract insight from various sources, including websites, documents, chat messages, emails, and more.
Keeping up with the available data has become difficult as the world becomes increasingly digitized. This information can be overwhelming, and it can take significant time to extract only the most relevant information from an article or document. NLP—based text summarization allows you to quickly and easily remove the most important information from lengthy texts. In today’s information-rich environment, it is a potent tool that may help you save time, increase productivity, and keep informed.
Extraction-Based Summarization
Text summarization is an important NLP task because much of the information we create and exchange is in written form. As a result, systems that can help us extract the core ideas from large amounts of text and preserve overall meaning stand to revolutionize whole industries. While there are many approaches to text summarization, they can be broadly classified into extractive and abstractive methods. Extractive methods select and combine existing sentences from a document to create a summary, while abstractive models generate new sentences. Text summary can be used to quickly create an abbreviated meeting transcript, for instance, if you frequently hold video conferences with your team to explore potential new products. It would be particularly useful for team members who cannot attend the meeting and need an easy way to catch up on the key takeaways.
Most practical text summarization systems are based on extractive methods. These systems identify the most important sentences in a document by assigning them a ranking score based on their relevance to the topic. Then, they select the top sentences to include in the summary and discard the rest. One of the most popular extractive summarization models is LexRank, which uses a simple text analysis method like TF-IDF to rank sentences by similarity to other sentences in the same document.
Abstractive Summarization
As the name suggests, abstractive text summarization is a more abstract approach to summarization. Instead of focusing on picking out important sentences or paragraphs, it uses machine learning and natural language processing to generate new phrases that accurately convey the original text’s key concept more concisely and coherently. It can be difficult to keep up with all the daily news and articles, let alone read them all, to find the most relevant information for your business. Text summarization is a great way to condense lengthy texts into short, digestible content containing the most important information.
This type of summarization is a very popular use case for AI-based summarizers. These Python-based tools can reduce the time and effort you have to spend reading lengthy articles or research reports by allowing you to quickly scan over the key points most relevant to your work. The most common method for abstractive summarization is to use sequence-to-sequence neural networks, such as transformer architectures. These models are trained end-to-end without any specialized data preparation or submodels and promise to be completely data-driven. However, other approaches for abstractive summarization use a more graphical representation of the sentence structure and word association, such as word embeddings.
Neural Networks
It can take a human days or weeks to read a 50-page technical document, filter out irrelevant material and write a complete summary without leaving any important details out. Machine learning can do this much faster and more accurately. Neural networks are one such type of deep learning model that has achieved state-of-the-art results for abstractive summarization. They can learn a language generation model specific to the source documents and produce results that rival or outperform other abstractive methods. These models can also be trained end-to-end, requiring no specialized vocabulary or expertly pre-processed source documents. It makes them highly practical and widely applicable to any business that uses text. Text summarization is just one of many Natural Language Processing tasks that can be automated. Whether you want to automate the task of summarizing news articles for your employees or create chapters for podcasts and YouTube videos, text summarization can help you make this happen. Start with a free 10-day trial of the platform to see how it can keep your team informed on all the most recent trends and advancements in your sector. It will enable you to make the best possible decisions swiftly and successfully.
