| dc.description.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. | en_US |
