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In this article, I will discuss Text Summarization Techniques and python APIs that we can use for this purpose. Basically, text summarization refers to retrieving the most significant and relevant information from a large piece of text. Furthermore, it is done computationally using some of the Machine Learning (ML) approaches.
Significantly, text summarization has a large number of applications in several domains. While, we can use it in summarizing book chapters, court judgments, news analysis, media monitoring, and video summarization. Further applications of text summarization include complaints analysis, helpdesk, and question answering bots. The following figure shows the two broad categories of automatic text summarization.
Extractive Summarization
While this approach doesn’t generate any new text. Basically, it works by extracting the relevant information from the original text. In other words, this approach selects sentences from the original document. Hence, it uses some techniques for ranking. So, it chooses the most relevant text. Further, this approach is much easier. It combines the keyphrases. Also, it may result in grammatical errors.
In order to perform extractive text summarization, there is a python library – gensim. Further, this library has a TextRank algorithm. Basically, this algorithm finds the frequency of words. Hence, more frequently appearing words are relevant. Another python library is sumy. It contains algorithms such as LexRank. Besides it, there are other algorithms such as Luhn and Latest Semantic Analysis (LSA). While LexRabk finds sentence similarity. Besides LSA is a Machine Learning technique. It is unsupervised in nature. Further, Luhn finds summaries using TF-IDF. Another method is KL-sum. It finds word distribution. Accordingly, it selects the text.
Abstractive Summarization
In contrast, abstractive summarization generates new text. So, it generates entirely new text. Hence, it is similar to human summarization. But it is more challenging. Also, it is more difficult to perform. In fact, deep learning approaches fall in this category. We can use it for headline generation. While pysummarization is one such library. It contains methods that use LSTM. Another python library is SpaCy.
Apart from the above methods, there is another method. It is called as aided summarization. Basically, it combines software and human efforts.
Further Reading – Python Libraries for Text Summarization Techniques
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