In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
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Question: Concept maps can be used to concisely represent important information and bring structure into large document collections. Therefore, we study a variant of multidocument 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.

Answer: Connecting the dots: Summarizing and Structuring Large Document Collections Using Concept Maps


Question: This paper presents Centre for Development of Advanced Computing Mumbai’s (CDACM) submission to NLP Tools Contest on Statistical Machine Translation in Indian Languages (ILSMT) 2015 (collocated with ICON 2015). The aim of the contest was to collectively explore the effectiveness of Statistical Machine Translation (SMT) while translating within Indian languages and between English and Indian languages. In this paper, we report our work on all five language pairs, namely Bengali-Hindi (bn-hi), Marathi-Hindi (mrhi), Tamil-Hindi (ta-hi), Telugu-Hindi (tehi), and English-Hindi (en-hi) for Health, Tourism and General domains. We have used suffix separation, compound splitting and preordering prior to SMT training and testing.

Answer: Statistical Machine Translation for Indian Languages: Mission Hindi 2


Question: While influence diagrams have many ad­ vantages as a representation framework for Bayesian decision problems, they have a se­ rious drawback in handling asymmetric de­ cision problems. To be represented in an influence diagram, an asymmetric decision problem must be symmetrized. A consid­ erable amount of unnecessary computation may be involved when a symmetrized influ­ ence diagram is evaluated by conventional al­ gorithms. In this paper we present an ap­ proach for avoiding such unnecessary compu­ tation in influence diagram evaluation.

Answer:
Solving Asymmetric Decision Problems with Influence Diagrams