Statistical Automatic Summarization in Organic Chemistry

We present an oriented numerical summarizer algorithm, applied to producing automatic summaries of scientific documents in Organic Chemistry. We present its implementation named Yachs (Yet Another Chemistry Summarizer) that combines a specific document pre-processing with a sentence scoring method relying on the statistical properties of documents. We show that Yachs achieves the best results among several other summarizers on a corpus of Organic Chemistry articles.

Adaptive Learning with Binary Neurons

A efficient incremental learning algorithm for classification tasks, called NetLines, well adapted for both binary and real-valued input patterns is presented. It generates small compact feedforward neural networks with one hidden layer of binary units and binary output units. A convergence theorem ensures that solutions with a finite number of hidden units exist for both binary and real-valued input patterns. An implementation for problems with more than two classes, valid for any binary classifier, is proposed. The generalization error and the size of the resulting networks are compared to the best published results on well-known classification benchmarks. Early stopping is shown to decrease overfitting, without improving the generalization performance.