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Abstract Meaning Representation (AMR) Annotation Release 3.0
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Abstract Meaning Representation (AMR) Annotation Release 3.0 ...
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Abstract Meaning Representation (AMR) Annotation Release 2.0
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Abstract Meaning Representation (AMR) Annotation Release 2.0 ...
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Abstract Meaning Representation (AMR) Annotation Release 1.0
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Abstract Meaning Representation (AMR) Annotation Release 1.0 ...
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Induction of Word and Phrase Alignments for Automatic Document Summarization ...
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Abstract:
Current research in automatic single document summarization is dominated by two effective, yet naive approaches: summarization by sentence extraction, and headline generation via bag-of-words models. While successful in some tasks, neither of these models is able to adequately capture the large set of linguistic devices utilized by humans when they produce summaries. One possible explanation for the widespread use of these models is that good techniques have been developed to extract appropriate training data for them from existing document/abstract and document/headline corpora. We believe that future progress in automatic summarization will be driven both by the development of more sophisticated, linguistically informed models, as well as a more effective leveraging of document/abstract corpora. In order to open the doors to simultaneously achieving both of these goals, we have developed techniques for automatically producing word-to-word and phrase-to-phrase alignments between documents and their ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.0907.0804 https://arxiv.org/abs/0907.0804
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ISI Chinese-English Automatically Extracted Parallel Text ...
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ISI Arabic-English Automatically Extracted Parallel Text ...
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Scalable Inference and Training of Context-Rich Syntactic Translation Models
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Scalable Inference and Training of Context-Rich Syntactic Translation Models ...
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