In this report, we present our extractive summarization system on both summarization and multiling tracks of TAC 2011. We introduce an extractive multi-document summarization method based on hierarchical topic model of hierarchical Latent Dirichlet Allocation (hLDA) and sentence compression. hLDA is a representative generative probabilistic model, which not only can mine latent topics from a la...

Gas leak detection is an important issue in infrastructure monitoring and industrial production. In this context, infrared (IR) absorption spectroscopy is a major measurement method. It can be applied in an extractive or remote detection scheme. Tunable laser spectroscopy (TLS) instruments are able to detect CH₄ leaks with column densities below 10 ppm·m from a distance of 30 m in less than a s...

We demonstrate TextRank – a system for unsupervised extractive summarization that relies on the application of iterative graphbased ranking algorithms to graphs encoding the cohesive structure of a text. An important characteristic of the system is that it does not rely on any language-specific knowledge resources or any manually constructed training data, and thus it is highly portable to new ...

Most approaches to extractive summarization define a set of features upon which selection of sentences is based, using algorithms independent of the features themselves. We propose a new set of features based on low-level, atomic events that describe relationships between important actors in a document or set of documents. We investigate the effect this new feature has on extractive summarizati...

We present two novel and contrasting Recurrent Neural Network (RNN) based architectures for extractive summarization of documents. The Classifier based architecture sequentially accepts or rejects each sentence in the original document order for its membership in the final summary. The Selector architecture, on the other hand, is free to pick one sentence at a time in any arbitrary order to pie...