Heading Generator using Machine Learning

Abstract

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Supervised learning is the type of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. We will be focusing on the following model in particular: Heading Generator using Machine Learning using the YouTube trending videos dataset and the Python programming language to train a model of text generation language using machine learning, which will be used for the task of Heading generator for youtube videos or even for your blogs. Heading generator is a natural language processing task and is a central issue for several machine learning, including text synthesis, speech to text, and conversational systems. To build a model for the task of Heading generator or a text generator, the model should be able to learn the probability of a word occurring, using words that have already appeared in the sequence as context. Headline or short summary generation is an important problem in Text Summarization and has several practical applications. We present a discriminative learning framework and a rich feature set for the headline generation task. Secondly, we present a novel Bleu measure based scheme for evaluation of headline generation models, which does not require human produced references. We achieve this by building a test corpus using the Google news service. We propose two stacked log-linear models for both headline word selection (Content Selection) and for ordering words into a grammatical and coherent headline.