This is an abstractive summarization demo program. It was mainly used to summarize opinions, but since it does not rely on any domain information, it can be used to summarize any highly redundant text.
MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
Speech technology potentially allows everyone to participate in today's information revolution and can bridge the language barrier gap. Unfortunately, construction of speech processing systems requires significant resources. With some 6900 languages in the world, traditionally speech processing is prohibitive to all but the most economically viable languages. In spite of recent improvements in speech processing, supporting new languages is a skilled job requiring significant effort from trained individuals. SPICE aims to overcome both limitations by providing an interactive language creation and evaluation toolkit that allows everyone to develop speech processing models, to collect appropriate data for model building, and to evaluate the results enabling iterative improvements.
NLTK — the Natural Language Toolkit — is a suite of open source Python modules, data and documentation for research and development in natural language processing. NLTK contains Code supporting dozens of NLP tasks, along with 40 popular Corpora and extensive Documentation including a 375-page online Book. Distributions for Windows, Mac OSX and Linux are available.