Automated natural language headline generation using discriminative machine learning models
Akshay Kishore Gattani · Summit (Simon Fraser University) · 2007
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 (Headline Synthesis).For decoding a beam search algorithm is used that combines the two log-linear models to produce a list of Ic-best human readable headlines from a news story.Systematic training and experimental results on the Google-news test dataset demonstrate the success and effectiveness of our approach.