Extractive Text Summarization of Kannada Text Documents Using Page Ranking Techniquedoi:10.1007/978-981-16-7610-9_51Text summarization is one of the core concepts of natural language processing techniques. Text summarization is a process of producing a quality summary statements from a document which ...
In the matrix, the weight of the word i in sentence j is defined by the entry aij. According to the TFIDF technique, each word in a sentence is given a certain weight, with zero being assigned to terms that aren’t included in the sentence. Indicator Representation Approaches Graph-Based...
The work presents a method to automatically generate a training dataset for the purpose of summarizing text documents with the help of feature extraction technique. The goal of this approach is to design a dataset which will help to perform the task of summarization very much like a human. A ...
Question‐driven automatic text summarization is a popular technique to produce concise and informative answers to specific questions using a document collection. Both query‐based and question‐driven summarization may not produce reliable summaries nor contain relevant information if they do not ...
For autonomous synthesis of medical reports from biomedical transcripts, this research proposes using an end‐to‐end summarization technique, Deep Dense Long Short Term Memory Network (LSTM), followed by Convolutional Neural Network (CNN).#Extensive testing, examination, and comparing have demonstrated ...
Application of Random Indexing (RI) to extractive text summarization has already been proposed in literature. RI is an approximating technique to deal with high-dimensionality problem of Word Space Models (WSMs). However, the distinguishing feature of RI from other WSMs (e.g. Latent Semantic ...
The proposed technique adopts the rough set approach of Reduct and Core calculation for a given data. Fuzzy core concept is used, when the Core is empty or whenever the Core has to be enhanced. The proposed technique is tested with DUC 2002 data Sets and has given good results....
We propose a summarization technique using dimension reduction based on singular value decomposition which effectively focuses on the most salient topics of each presentation. With this technique, sentence location information, which is used for text summarization, is combined to extract important sentences...
The extractive summarization is good at selecting important sentences so that we use them to guide the learning process of the sentence-level attention. In this paper, we present a novel multi-task learning-based technique for abstractive summarization which incorporates extractive summarization as an ...
Also, we performed a manual evaluation to measure the quality of summaries to validate our techniques. The proposed approach is found suitable for generating summarized Odia text and the same technique can also extend to other low-resource languages for extractive summarization system....