Advancements in Neural Network-Based Text Summarization Techniques

Automatic text summarization is a technique that draws out important and relevant information from a text to present a summary of it. This can be done using abstract or extractive summarization techniques. Abstract summarization includes the generation of new sentences expressing the core ideas, which are accomplished through methods such as Text Frequency Ranking Sentence Prediction. On the other hand, extractive summarization chooses important sentences or phrases from the original text. Extractive summarization methods include Term Frequency – Inverse Document Frequency, which gives a measure of the importance of words relative to a collection of documents; TextRank, a ranking algorithm that ranks sentences by relevance; and Seq2Seq models that use neural networks to produce summaries learning from training data. These are all methods that allow ATS systems to summarize extensive texts efficiently and permit users to get hold of the essential information quickly. Extractive summarization, on the other hand, involves selecting critical information or sentences from the original document and using such selected sentences to form a summary. This paper aims to introduce a novel statistical approach based on the extractive summarization of a single document. Based on this method, the associated method extracts sentences according with abstract meaning from the input text. Such weights are calculated for each sentence so that the order of the sentences used within the summary can be decided upon. This selection of the highest-ranking sentences will ensure that the most important phrases are included from which a relatively good quality summary of the source text is generated. An audio file consisting of this is then created.