In this paper we present our task-based evaluation of query biased summarization for cross-language information retrieval (CLIR) using relevance prediction. We describe our 13 summarization methods each from one of four summarization strategies. We show how well our methods perform using Farsi text from the CLEF 2008 shared-task, which we translated to English automtatically. We report precision/recall/F1, accuracy and time-on-task. We found that different summarization methods perform optimally for different evaluation metrics, but overall query biased word clouds are the best summarization strategy. In our analysis, we demonstrate that using the ROUGE metric on our sentence-based summaries cannot make the same kinds of distinctions as our evaluation framework does. Finally, we present our recommendations for creating muchneeded evaluation standards and datasets.
REF provides a taskbased evaluation of multiple query biased summarization methods for cross-language information retrieval using relevance prediction.
Finding Good Enough: A Task-Based Evaluation of Query Biased Summarization for Cross-Language Information Retrieval
EMNLP
Computer Science