Karolina Owczarzak

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Automatic evaluation has greatly facilitated system development in summarization. At the same time, the use of automatic evaluation has been viewed with mistrust by many, as its accuracy and correct application are not well understood. In this paper we provide an assessment of the automatic evaluations used for multi-document summarization of news. We(More)
In this paper we present a novel method for deriving paraphrases during automatic MT evaluation using only the source and reference texts, which are necessary for the evaluation, and word and phrase alignment software. Using target language paraphrases produced through word and phrase alignment a number of alternative reference sentences are constructed(More)
We present a method for evaluating the quality of Machine Translation (MT) output, using labelled dependencies produced by a Lexical-Functional Grammar (LFG) parser. Our dependencybased method, in contrast to most popular string-based evaluation metrics, does not unfairly penalize perfectly valid syntactic variations in the translation, and the addition of(More)
In this paper we show how labelled dependencies produced by a Lexical-Functional Grammar parser can be used in Machine Translation evaluation. In contrast to most popular evaluation metrics based on surface string comparison, our dependency-based method does not unfairly penalize perfectly valid syntactic variations in the translation, shows less bias(More)
We present a novel method for evaluating the output of Machine Translation (MT), based on comparing the dependency structures of the translation and reference rather than their surface string forms. Our method uses a treebank-based, widecoverage, probabilistic Lexical-Functional Grammar (LFG) parser to produce a set of structural dependencies for each(More)
In this paper, we present a novel approach to combine the outputs of multiple MT engines into a consensus translation. In contrast to previous Multi-Engine Machine Translation (MEMT) techniques, we do not rely on word alignments of output hypotheses, but prepare the input sentence for multi-engine processing. We do this by using a recursive decomposition(More)
Abstractive summarization has been a longstanding and long-term goal in automatic summarization, because systems that can generate abstracts demonstrate a deeper understanding of language and the meaning of documents than systems that merely extract sentences from those documents. Genest (2009) showed that summaries from the top automatic summarizers are(More)