A Machine Learning Approach to Sentence Ordering for Multidocument Summarization and Its Evaluation

Ordering information is a difficult but a important task for natural language generation applications. A wrong order of information not only makes it difficult to understand, but also conveys an entirely different idea to the reader. This paper proposes an algorithm that learns orderings from a set of human ordered texts. Our model consists of a set of ordering experts. Each expert gives its precedence preference between two sentences. We combine these preferences and order sentences. We also propose two new metrics for the evaluation of sentence orderings. Our experimental results show that the proposed algorithm outperforms the existing methods in all evaluation metrics.

• Type: Conference
• Year: 2005
• Where: IJCNLP
• Authors: Danushka Bollegala, Naoaki Okazaki, Mitsuru Ishizuka
• Keywords: Human Ordered Texts, IJCNLP 2005, Natural Language Generation, Natural Language Processing, Wrong Order