Automatic Keyword Extraction for Text Summarization: A Survey <s> DATABASES: A REVIEW <s> In this paper, experiments on automatic extraction of keywords from abstracts using a supervised machine learning algorithm are discussed. The main point of this paper is that by adding linguistic knowledge to the representation (such as syntactic features), rather than relying only on statistics (such as term frequency and n-grams), a better result is obtained as measured by keywords previously assigned by professional indexers. In more detail, extracting NP-chunks gives a better precision than n-grams, and by adding the PoS tag(s) assigned to the term as a feature, a dramatic improvement of the results is obtained, independent of the term selection approach applied. <s> BIB001 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> DATABASES: A REVIEW <s> Objective:: The aim of this paper is to survey the recent work in medical documents summarization. Background:: During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc. Methodology:: This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics. Discussion and conclusions:: The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications. <s> BIB002
In the literature, we observed that, there are seven types of databases used for text summarization process namely relevant to each topic from the AQUAINT corpus for queryrelevant multi-document summarization BIB001 . MEDLINE/ PubMed dataset is a baseline repository that links 19 million articles to with http://dx.doi.org/ article identifiers and http://crossref.org/ with journal identifiers [130] and it contains abstracts from more than 3500 journals BIB002 . MEDLINE also provides keyword searches and returns abstracts that contain the keywords. The TAC is the international conference for performance evaluation in the area of subparts of Natural Language Processing such as text summarization. Usually, TAC-2009, 2010 and 2011 conference dataset used for text summarization in past. The CL-SciSumm Shared Task is run off the CL-SciSumm corpus, and comprises three sub-tasks in automatic research paper summarization on a new corpus of research papers. A training corpus of twenty topics and a test corpus of ten topics were released. The topics comprised of ACL Computational Linguistics research papers, and their citing papers and three output summaries each. The three output summaries comprise: the traditional self-summary of the paper (the abstract), the community summary (the collection of citation sentences 'citances') and a human summary written by a trained annotator [132] . TIPSTER is a corpus of 183 documents from the Computation and Language (cmp-lg) collection has been marked up in xml and made available as a general resource to the information retrieval, extraction, and summarization communities.