Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps

Concept maps can be used to concisely represent important information and bring structure into large document collections. Therefore, we study a variant of multi-document summarization that produces summaries in the form of concept maps. However, suitable evaluation datasets for this task are currently missing. To close this gap, we present a newly created corpus of concept maps that summarize heterogeneous collections of web documents on educational topics. It was created using a novel crowdsourcing approach that allows us to efficiently determine important elements in large document collections. We release the corpus along with a baseline system and proposed evaluation protocol to enable further research on this variant of summarization.Comment: Published at EMNLP 201

Generating Concise and Readable Summaries of XML Documents

XML has become the de-facto standard for data representation and exchange, resulting in large scale repositories and warehouses of XML data. In order for users to understand and explore these large collections, a summarized, bird's eye view of the available data is a necessity. In this paper, we are interested in semantic XML document summaries which present the "important" information available in an XML document to the user. In the best case, such a summary is a concise replacement for the original document itself. At the other extreme, it should at least help the user make an informed choice as to the relevance of the document to his needs. In this paper, we address the two main issues which arise in producing such meaningful and concise summaries: i) which tags or text units are important and should be included in the summary, ii) how to generate summaries of different sizes.%for different memory budgets. We conduct user studies with different real-life datasets and show that our methods are useful and effective in practice

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems