@article{Joachims:2007,
        title = {Search Engines that Learn from Implicit Feedback},
        author = {Joachims, Thorsten and Radlinski, Filip},
        journal = {Computer},
        number = 8,
        pages = {34-40},
        volume = 40,
        year = 2007,
        url = {http://doi.ieeecomputersociety.org/10.1109/MC.2007.289},
        abstract = {Search-engine logs provide a wealth of information that machine-learning techniques can harness to improve search quality. With proper interpretations that avoid inherent biases, a search engine can use training data extracted frmo the logs to automatically tailor ranking functions to a particular user group or collection.},
	biburl = {http://www.bibsonomy.org/bibtex/20cf553465d23b0671006fbe975aaf6b6/diego_ma},
	keywords = {searchfeedbackweb_search},
    library = {Mine (March 2010) - in original magazine}, doi = {10.1109/MC.2007.289}}

@article{Ramirez-Cruz:2009,
        title = {Describing Biomedical Document Sets in Terms of its Most Distinctive Facts},
        author = {Ram{\'\i}rez-Cruz, Yunior and Berlanga-Llavori, Rafael and Pons-Porrata, Aurora},
        journal = {Procesamiento del Lenguaje Natural},
        pages = {159-167},
        volume = 43,
        year = 2009,
        url = {http://www.sepln.org/revistaSEPLN/revista/43/articulos/art18.pdf},
        abstract = {In this paper, we propose a method to describe a set of conceptually indexed biomedical documents in terms of its most distinctive facts. These documents are retrieved to support the occurrence of a focus concept, which expresses an information need. The facts used for description are concise information units, represented as triples of the form entity-verb-entity. These are presented as a ranked list, ordered by their relevance with respect to the focus concept, which is determined using a language modeling approach. Experimental results, obtained on three document sets over a collection extracted from MEDLINE, are promising. Keywords: text mining, information retrieval, biomedical applications.},
	biburl = {http://www.bibsonomy.org/bibtex/23485a7c774c59b4e4a3654c1add794c6/diego_ma},
	keywords = {biomedicalretrieval},
    library = {Mine (March 2010)}}

@article{Villa:2009,
        title = {Propuesta y evaluaci{\'o}n de un m{\'e}todo extractivo de generaci{\'o}n de res{\'u}menes en el {\'a}mbito biom{\'e}dico basado en conceptos},
        author = {de la Villa, Manuel and Ma{\~n}a, Manuel J.},
        journal = {Procesamiento del Lenguaje Natural},
        pages = {131-139},
        volume = 43,
        year = 2009,
        url = {http://www.sepln.org/revistaSEPLN/revista/43/articulos/art15.pdf},
        abstract = {Los métodos de generación de resúmenes basados en técnicas extractivas han demostrado ser muy útiles por su adaptabilidad y eficiencia en tiempo de respuesta en cualquier tipo de dominios. En el ámbito biomédico son numerosos los estudios que hablan de la sobrecarga de información y recogen la necesidad de aplicación de técnicas eficientes de recuperación y generación de resúmenes para una correcta aplicación de la medicina basada en la evidencia. En este contexto vamos a presentar una propuesta metodológica de generación automática de resúmenes basada en ontologías y grafos, aplicando técnicas de similitud y la frecuencia de aparición de los conceptos para obtener las frases más relevantes. Se realiza una evaluación de la propuesta frente a otras metodologías con la herramienta ROUGE y se analizan los resultados. Aunque la extensión del conjunto de evaluación no permite extraer conclusiones significativas, los resultados son suficientemente prometedores como para confiar en la efectividad de la propuesta presentada.},
	biburl = {http://www.bibsonomy.org/bibtex/20a2c82b17c9731b7daf197b8ccb10957/diego_ma},
	keywords = {summarisationbiomedical},
    library = {Mine (March 2010)}}

@inproceedings{Sauper:2009,
        title = {Automatically Generating Wikipedia Articles: A Structure-Aware Approach},
        author = {Christina Sauper and Barzilay, Regina},
        booktitle = {Proceedings ACL 2009},
        year = 2009,
        url = {http://people.csail.mit.edu/regina/papers.html},
        abstract = {In this paper, we investigate an approach for creating a comprehensive textual overview of a subject composed of information drawn from the Internet. We use the high-level structure of human-authored texts to automatically induce a domain-specific template for the topic structure of a new overview. The algorithmic innovation of our work is a method to learn topic-specific extractors for content selection jointly for the entire template. We augment the standard perceptron algorithm with a global integer linear programming formulation to optimize both local fit of information into each topic and global coherence across the entire overview. The results of our evaluation confirm the benefits of incorporating structural information into the content selection process.},
	biburl = {http://www.bibsonomy.org/bibtex/2119337cf4cab9c308c5f781c4fa0af48/diego_ma},
	keywords = {summarisationmultidocumentgenerationwikipedia},
    library = {Mine (March 2010)}}

@article{Barzilay:2005,
        title = {Sentence Fusion for Multidocument News Summarization},
        author = {Regina Barzilay and Kathleen R. McKeown},
        journal = {Computational Linguistics},
        number = 3,
        pages = {297--328},
        volume = 31,
        year = 2005,
        
      month = {September},
    biburl = {http://www.bibsonomy.org/bibtex/2677204ee6eac41e7b1dea8ca8cbbdd67/diego_ma},
	keywords = {summarisation},
    library = {Mine (March 2010)}}

@article{lin:2006,
        title = {Methods for automatically evaluating answers to complex questions.},
        author = {Jimmy J. Lin and Dina Demner-Fushman},
        journal = {Inf. Retr.},
        number = 5,
        pages = {565-587},
        volume = 9,
        year = 2006,
        url = {http://dblp.uni-trier.de/db/journals/ir/ir9.html#LinD06},
        biburl = {http://www.bibsonomy.org/bibtex/2272c15bbe86710c9f77a53141f757774/diego_ma},
	keywords = {clustering biomedical question_analysis},
    }

@inproceedings{Wan:2009c,
        title = {Improving Grammaticality in Statistical Sentence Generation: Introducing a Dependency Spanning Tree Algorithm with an Argument Satisfaction Model},
        address = {Athens, Greece},
        author = {Stephen Wan and Mark Dras and Robert Dale and C{\'e}cile Paris},
        booktitle = {Proceedings of Conference of the European Chapter of the Association for Computational Linguistics(EACL 2009)},
        year = 2009,
        biburl = {http://www.bibsonomy.org/bibtex/2c20b8947606623244cd6e574a69af71e/diego_ma},
	keywords = {summarisation},
    }

@inproceedings{Wan:2009b,
        title = {Capturing the User's Reading Context for Tailoring Summaries},
        author = {C. Paris and S. Wan},
        booktitle = {Proceedings of the International Conference on User Modelling, Adaptation and Presentation (UMAP 2009)},
        year = 2009,
        url = {http://www.ict.csiro.au/staff/cecile.paris/distribution/Paris-Wan-Final-UMAP09.pdf},
        abstract = {The web has become a major source of information to learn about a  topic. With the continuous growth of information and its high connectivity, it is hard to follow only the links that are relevant and not to get lost in hyperspace. Our  aim  is  to  support  people  who  read  documents  in  a  highly  connected information  space,  helping  them  remain  on  focus.  Our  contextually-aware  in-browser  text  summarisation  tool,  IBES,  does  this  by  capturing  users?  current interests   and   providing   users   with   contextualised   summaries   of   linked documents, to help them decide whether the link is worth following.},
	biburl = {http://www.bibsonomy.org/bibtex/2eb94394e92ff00a46204ac735d6adb89/diego_ma},
	keywords = {summarisation},
    }

@inproceedings{Qazvinian:2008,
        title = {Scientific paper summarization using citation summary networks},
        address = {Manchester, United Kingdom},
        author = {Vahed Qazvinian and Dragomir R. Radev},
        booktitle = {Proceedings of the 22nd International Conference on Computational Linguistics},
        pages = {689-696},
        year = 2008,
        url = {http://portal.acm.org/citation.cfm?id=1599168},
        abstract = {Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a model of summarizing a single article, which can be further used to summarize an entire topic. Our model is based on analyzing others' viewpoint of the target article's contributions and the study of its citation summary network using a clustering approach.},
	biburl = {http://www.bibsonomy.org/bibtex/24bbfb7d36874740e2da072fa9842202c/diego_ma},
	keywords = {jabref:noKeywordAssigned},
    }

@inproceedings{Wan:2009,
        title = {Whetting the Appetite of Scientists: Producing Summaries Tailored to the Citation Context},
        address = {Austin, Texas},
        author = {Stephen Wan and C{\'e}cile Paris and Robert Dale},
        booktitle = {Proceedings of the 2009 Joint Conference on Digital Libraries},
        pages = {59-69},
        year = 2009,
        biburl = {http://www.bibsonomy.org/bibtex/2d944546bd20383c058a8c2c2a1dd59db/diego_ma},
	keywords = {summarisation},
    }

@inproceedings{Hammouda:2005,
        title = {CorePhrase: keyphrase extraction for document clustering},
        author = {Khaled M. Hammouda and Diego N. Matute and Mohamed S. Kamel},
        booktitle = {Proceedings of MLDM},
        pages = {265--274},
        year = 2005,
        biburl = {http://www.bibsonomy.org/bibtex/276e0ca30cc51cabf1707ffcb48b02fd2/diego_ma},
	keywords = {clustering},
    }

@article{Gutwin:1999,
        title = {Improving browsing in digital libraries with keyphrase indexes},
        author = {Carl Gutwin and Gordon Paynter and Ian Witten and Craig Nevill-Manning and Eibe Frank},
        journal = {Journal of Decision Support Systems},
        pages = {81--104},
        volume = 27,
        year = 1999,
        biburl = {http://www.bibsonomy.org/bibtex/238d1409e43319b97c7ce94c130a70e4f/diego_ma},
	keywords = {jabref:noKeywordAssigned},
    }

@inproceedings{Kim:2006,
        title = {Interpreting Semantic Relations in Noun Compounds via Verb Semantics},
        address = {Sydney, Australia},
        author = {Su Nam Kim and Timothy Baldwin},
        booktitle = {Proceedings of Proceedings of the 44th Annual Meeting of the Association for Computational Linguistics and 21st International Conference on Computational Linguistics},
        pages = {491--498},
        year = 2006,
        biburl = {http://www.bibsonomy.org/bibtex/2eb7cbacebac2fdb6f4cb93c733e08f2b/diego_ma},
	keywords = {jabref:noKeywordAssigned},
    }

@inproceedings{Kim:2005,
        title = {Automatic Interpretation of Compound Nouns using {WordNet} Similarity},
        address = {Jeju, Korea},
        author = {Su Nam Kim and Timothy Baldwin},
        booktitle = {Proceedings of 2nd International Joint Conference on Natual Language Processing},
        pages = {945--956},
        year = 2005,
        biburl = {http://www.bibsonomy.org/bibtex/2287bfcf4b11f05e6bae4b6c7d4f56f41/diego_ma},
	keywords = {compound_nouns WordNet},
    }

@inproceedings{Moldovan:2004b,
        title = {Models for the Semantic Classification of Noun Phrases},
        address = {Boston, USA},
        author = {Dan Moldovan and Adriana Badulescu and Marta Tatu and Daniel Antohe and Roxana Girju},
        booktitle = {Proceedings of the HLT-NAACL 2004: Workshop on Computational Lexical Semantics},
        pages = {60--67},
        year = 2004,
        biburl = {http://www.bibsonomy.org/bibtex/21755034ab1f04fd58519dec45f55790e/diego_ma},
	keywords = {compound_nouns},
    }

@inproceedings{Lawrie:2001,
        title = {Finding Topic Words for Hierarchical Summarization},
        address = {New Orleans, Louisiana, USA},
        author = {Dawn Lawrie and W. Bruce Croft and Arnold Rosenberg},
        booktitle = {Proceedings of SIGIR 2001},
        pages = {349--357},
        year = 2001,
        biburl = {http://www.bibsonomy.org/bibtex/2bf433c7d3f8fcbcf50b564c646356c66/diego_ma},
	keywords = {summarisation},
    }

@inproceedings{Barzilay:1997,
        title = {Using lexical chains for text summarization},
        author = {Regina Barzilay and Michael Elhadad},
        booktitle = {Proceedings of the ACL/EACL 1997 Workshop on Intelligent Scalable Text Summarization},
        pages = {10--17},
        year = 1997,
        biburl = {http://www.bibsonomy.org/bibtex/20be4f21418a1236d4225a54359455331/diego_ma},
	keywords = {summarisation},
    }

@inproceedings{DAvanzo:2005,
        title = {A Keyphrase-Based Approach to Summarization:the LAKE System},
        author = {Ernesto D\'Avanzo and Bernado Magnini},
        booktitle = {Proceedings of Document Understanding Conferences},
        pages = {6-8},
        year = 2005,
        biburl = {http://www.bibsonomy.org/bibtex/23d8c8f82f1f12bf80f4096f8d867c50c/diego_ma},
	keywords = {summarisation},
    }

@inproceedings{Tutos:2010,
        title = {A Study on the Use of Search Engines for Answering Clinical Questions},
        author = {Andreea Tutos and Moll{\'a}, Diego},
        booktitle = {Proceedings HIKM 2010},
        pages = {8 pages},
        year = 2010,
        abstract = {This paper describes an evaluation of the answerability of a set of clinical questions posed by physicians. The clinical questions belong to two categories of the five-leaf high-level hierarchical Evidence Taxonomy created by Ely and his colleagues: Intervention and Non Intervention. The questions are passed to two search engines (PubMed, Google), two question-answering systems (MedQA, Answers.com's BrainBoost), and a dictionary (OneLook) for locating the answers to the question corpus. The output of the systems is judged by a human and scored according to the Mean Reciprocal Rank (MRR). The results show the need for question modification and analyse the impact of specific types of modifications. The results also show that No Intervention questions are easier to answer than Intervention questions. Further, generic search engines like Google obtain higher MRR than specialised systems and even higher than a version of Google based on specialised literature (PubMed) only. In addition, an analysis of the location of the answer in the returned documents is provided.},
	biburl = {http://www.bibsonomy.org/bibtex/278bc9c70c5f106b63ed6e7635feda7a5/diego_ma},
	keywords = {search inf-retr question_answering biomedical molla_publication},
    }

@conference{X-BioNLP:2002,
        title = {Proceedings of the ACL-02 Workshop on Natural Language Processing in the Biomedical Domain},
        author = {Johnson, Stephen},
        year = 2002,
        url = {http://www.aclweb.org/anthology-new/W/W02/#0300},
        biburl = {http://www.bibsonomy.org/bibtex/20ee3dc5d1a9edb44b520e78bc73338a8/diego_ma},
	keywords = {biomedical nat_lang},
    }