Text summarization is one of application of natural language processing and is becoming more popular for information condensation. Text summarization is a process of reducing the size of original document and producing a summary by retaining important information of original document. This paper gives comparative study of various text summarization methods based on different types of application. The paper discusses in detail two main categories of text summarization methods these are extractive and abstractive summarization methods. The paper also presents taxonomy of summarization systems and statistical and linguistic approaches for summarization.

Due to increase in amount of Hindi content on the web in past years, there are more requirements to perform sentiment analysis for Hindi Language. Sentiment Analysis (SA) is a task which finds orientation of one's opinion in a piece of information with respect to an entity. It deals with analyzing emotions, feelings, and the attitude of a speaker or a writer from a given piece of information. Sentiment Analysis involves capturing of user's behavior, likes and dislikes of an individual from the text. The work of most of the SA system is to identify the sentiments express over an entity, and then classify it into either positive or negative sentiment. Our proposed system for sentiment analysis of Hindi movie review uses HindiSentiWordNet (HSWN) to find the overall sentiment associated with the document; polarity of words in the review are extracted from HSWN and then final aggregated polarity is calculated which can sum as either positive, negative or neutral. Synset replacement algorithm is used to find polarity of those words which don't have polarity associated with it in HSWN. Negation and discourse relations which are mostly present in Hindi movie review are also handled to improve the performance of the system.

This paper presents a survey of Machine translation system for Indian Regional languages. Machine translation is one of the central areas of Natural language processing (NLP). Machine translation (henceforth referred as MT) is important for breaking the language barrier and facilitating inter-lingual communication. For a multilingual country like INDIA which is largest democratic country in whole world, there is a big requirement of automatic machine translation system. With the advent of Information Technology many documents and web pages are coming up in a local language so there is a large need of good MT systems to address all these issues in order to establish a proper communication between states and union governments to exchange information amongst the people of different states. This paper focuses on different Machine translation projects done in India along with their features and domain.

Named entity recognition is a process and study of identification of entities that are proper nouns and classifying them to their appropriate pre-defined class, also called as tag. Named entity recognition is also called as entity chunking, entity identification and entity extraction. It is a sub task of information extraction, where structured text is extracted from unstructured text. Popular applications of NER are machine translation, text mining, data classification, question answering system. This paper presents a survey of different NERC techniques, approach, observations and features for

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