machine learning LexRank method for Text Summarization LexRank method for text summarization is another child method to PageRank method similar to TextRank. It uses a graph based approach for text summarization
machine learning Graph based approach for Text summarization (Reduction) In this article we will understand Graph based approach for text summarization (also known as Graph Reduction). It uses techniques to reducing graph size such as predicate-argument mapping and normalization.
machine learning Edmundson Heuristic Method for text summarization Edmundson Heuristic Method proposes the use of a subjectively weighted combination of features as opposed to traditionally used feature weights generated using a corpus
machine learning Luhn’s Heuristic Method for text summarization The idea of Luhn’s Heuristic Method for text summarization is that any sentence with maximum occurrences of the highest frequency words(Stopwords) and least occurrences are not important to the meaning of the document
machine learning KL Sum algorithm for text summarization Kullback-Lieber (KL) Sum algorithm for text summarization which focuses on minimization of summary vocabulary by checking the divergence from the input vocabulary.
machine learning Latent Semantic Analysis for text summarization Latent Semantic Analysis is an efficient technique for text summarization in order to abstract out the hidden context of the document.
machine learning SumBasic algorithm for text summarization SumBasic is an algorithm to generate multi-document text summaries. Basic idea is to utilize frequently occuring words in a document than the less frequent words so as to generate a summary
software engineering Abstract Base class in Python In this article we will discuss about Abstract base classes in Python. Abstract classes are an implementation of Abstraction in Object Oriented approach.
