Aurélien Bossard

CBSEAS, a Summarization System -- Integration of Opinion Mining Techniques to Summarize Blogs

In this paper, we present a novel approach for automatic summarization. Our system, called CBSEAS, integrates a new method to detect redundancy at its very core, and produce more expressive summaries than previous approaches. Moreover, we show that our system is versatile enough to integrate opinion mining techniques, so that it is capable of producing opinion oriented summaries. The very competitive results obtained during the last Text Evaluation Conference (TAC 2008) show that our approach is efficient.

Combining a Multi-Document Update Summarization System –CBSEAS– with a Genetic Algorithm

In this paper, we present a combination of a multi-document summarization system with a genetic algorithm. We first introduce a novel approach for automatic summarization. CBSEAS, the system which implements this approach, integrates a new method to detect redundancy at its very core in order to produce summaries with a good informational diversity. However, the evaluation of our system at TAC 2008—Text Analysis Conference—revealed that system adaptation to a specific domain is fundamental to obtain summaries of an acceptable quality.

Using document structure for automatic summarization

In this paper, we present a novel approach for automatic summarization. We believe redundancy is the most important factor in building a summary automatically. We want to detect it automatically with an unsupervized method that could apply to any multi-document summarization task. CBSEAS, the system implementing our approach integrates a new method to detect redundancy at its very core, in order to produce more expressive summaries than previous approaches. However, the evaluation of our system at TAC 2008 –Text Analysis Conference– revealed some failings. We propose to make up for these weaknesses by using document structure inside the automatic summarizer.

An Evolutionary Algorithm for Automatic Summarization.

This paper proposes a novel method to select sentences for automatic summarization based on an evolutionary algorithm. The algorithm explores candidate summaries space following an objective function computed over ngrams probability distributions of the candidate summary and the source documents. This method does not consider a summary as a stack of independent sentences but as a whole text, and makes use of advances in unsupervised summarization evaluation. We compare this sentence extraction method to one of the best existing methods which is based on integer linear programming, and show its efficiency on three different acknowledged corpora.