The object of this project is to train a model to detect the topic of a given text. In the domain of automatic e-reporting and e-reputation detection, we expect that the documents we grabbed from Internet are topic concentrated. For exemple, we want to grab news about primary material cobalt industry. However, documents grabbed by key word "cobalt" may actually talk about Cobalt Air. By using Topic Modeling method to vectorize a natural language text, we could operate a semantic analysis and quantify text documents similarity. Thus, we could build a classifier to detect a document's topic, so that we could filter documents before using them for following work.

1. Copy the document whose topic you want to detect into the file todetect.txt under the root directory of project. The text has to be in English, and no preprocessing is required.

2. Run the script topicDetection.py under the same directory.

3. Copy the document you want to summerize into the file autoSum.txt under the root directory of project. The text has to be in English and has to be preprocessed as a single line string(!important!). Then run the script autoSum.py which implements the modified TextRank algorithm. For comparaison, running the script gensimAutoSum.py which uses autosummerization module of Gensim will give the result.

1. Due to the lack of labeled documents, the model can only project a document into one of these 4 topics cited above. Even if the document provided has nothing with these topics, it will still be predicted as one of these topics.

1. Add more labeled documents about more topics to enlarge the topic diversity of model.

2. Add the fonctionality to predict a document as "UNKNOWN" when the document has a weak similarity to all topics existing in the model.