Open AccessSaliency Detection in Text Documents using Policy-Driven Reinforcement Learning Methodologies
Author(s) -
Gaurav Meena,
Sarika Choudhary,
Ravi Raj Choudhary
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1020/1/012019
Subject(s) - computer science , feature (linguistics) , artificial intelligence , word (group theory) , relevance (law) , spell , feature engineering , natural language processing , pattern recognition (psychology) , value (mathematics) , root (linguistics) , vocabulary , reinforcement learning , machine learning , mathematics , deep learning , philosophy , linguistics , geometry , sociology , political science , anthropology , law
As the amount of information grows, it is challenging to find concise information. Thus it is necessary to build a system that could present human quality summaries. Saliency detection is a tool that provides abstracts or keywords of a given document. In this paper, three different approaches have been implemented for saliency detection. In all these three approaches, sentences are represented as a feature vector. In the first approach, features like root words, vocabulary intersections, words, and inclusion of numerical data use. This model is trained by using general Algorithms, Like Porter’s Stemmer, Spell check. In the second approach, apart from the features used in the first approach, TF-IDF scores, Mean, Standard Deviation, and a Threshold value of a word is also used as features. In the third approach, Maximal Marginal Relevance (MMR) algorithm is used to generate a summary.