Automatic Keyword Extraction for Text Summarization: A Survey <s> Query-based Text Summarization <s> We present two distinct text analysis systems. We first present two supervised sentence ranking approaches for use in extractive update summarization. For the first, we use the same general machine learning approach described in Fisher and Roark (2008) for update summarization. In the second, we use a similar machine learning approach, but include sub-sentential units produced by our discourse segmenter, see Fisher and Roark (2007b), as possible units for inclusion in a summary. Interestingly, we find that one approach performs significantly better in the production of the base summary, while the other approach performs significantly better in the update summary. We then present a large-corpus entity linking system. This system expands queries using internal links within Wikipedia and link entities with minimum-spanning-tree clustering. We present and evaluate empirical results on the TAC 2009 knowledge-base-population data, and demonstrate competitive results with a simple system. <s> BIB001 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Query-based Text Summarization <s> In web search, recency ranking refers to ranking documents by relevance which takes freshness into account. In this paper, we propose a retrieval system which automatically detects and responds to recency sensitive queries. The system detects recency sensitive queries using a high precision classifier. The system responds to recency sensitive queries by using a machine learned ranking model trained for such queries. We use multiple recency features to provide temporal evidence which effectively represents document recency. Furthermore, we propose several training methodologies important for training recency sensitive rankers. Finally, we develop new evaluation metrics for recency sensitive queries. Our experiments demonstrate the efficacy of the proposed approaches. <s> BIB002
In this summarization technique, a particular portion is utilized to extract the essential keyword from input document to make the summary of corresponding document  BIB001 developed a querybased summarization system that uses a log-linear model to classify each word in a sentence. It exploits the property of sentence ranking methods in which they consider neural query ranking and query-focused ranking. Dong et al. BIB002 developed a query-based summarization that uses document ranking, time-sensitive queries and ranks recency sensitive queries as the features for text summarization.