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The analysis of a follow) Tj 200.25 0 TD -0.2467 Tc 0 Tw (-) Tj 3 0 TD 0.375 Tc 0.1875 Tw (on ) Tj -203.25 -11.25 TD 0.0805 Tc 1.732 Tw (study, which focused on the use of sources during ) Tj 0 -11.25 TD 0.0815 Tc 3.481 Tw (writing, identified important requirements for the) Tj 0 Tc -0.9375 Tw ( ) Tj 0 -12 TD 0.1142 Tc -0.3017 Tw (support of source mat) Tj 86.25 0 TD 0.171 Tc 0 Tw (e) Tj 4.5 0 TD 0.1749 Tc -0.3624 Tw (rial capture:) Tj 48 0 TD 0 Tc -0.1875 Tw ( ) Tj -138.75 -11.25 TD ( ) Tj 0 -12 TD /F3 9.75 Tf 0.171 Tc 0 Tw (??) 0000410671 00000 n The paper focuses on the behavior and performance In: Proceedings of the 49th annual meeting of the Association for Computational Linguistics: human language technologies. In Advances in Cryptology—Crypto '90. 0000409664 00000 n Tj 20.25 0 TD 0 Tc -0.1875 Tw ( ) Tj -109.5 -11.25 TD /F0 9.75 Tf ( ) Tj 0 -12.75 TD /F1 12 Tf 0.055 Tc 0 Tw (Introduction) Tj 64.5 0 TD 0 Tc ( ) Tj -57 -12.75 TD /F0 9.75 Tf 0.1307 Tc 0.3247 Tw (People from virtually every branch of professional ) Tj -7.5 -11.25 TD 0.1417 Tc 0.4208 Tw (work author electronic documents while referring ) Tj 205.5 0 TD 0.3322 Tc 0.2303 Tw (to ) Tj -205.5 -11.25 TD 0.1361 Tc 0.1452 Tw (paper documents such as books, articles and reports. ) Association for Computational Linguistics, Sofia, pp 131–136, Ranzato M, Chopra S, Auli M, Zaremba W (2016) Sequence level training with recurrent neural networks. 0000408162 00000 n 0000449479 00000 n Association for Computational Linguistics, Lisbon, pp 1984–1989, Kågebäck M, Mogren O, Tahmasebi N, Dubhashi D (2014) Extractive summarization using continuous vector space models. 0000413914 00000 n 0000499522 00000 n The portable document format, or .pdf, is the most common and versatile format to send business documents. Recent advances in document summarization. Association for Computational Linguistics, Doha, pp 775–786, Chopra S, Auli M, Rush AM (2016) Abstractive sentence summarization with attentive recurrent neural networks. 0000144375 00000 n 0000424667 00000 n Lecture Notes in Computer Science, vol. Association for Computational Linguistics, Lisbon, pp 128–137, Gu J, Lu Z, Li H, Li VO (2016) Incorporating copying mechanism in sequence-to-sequence learning. 0000462954 00000 n 0000458453 00000 n J Artif Intell Res 22:457–479, Fang Y, Teufel S (2014) A summariser based on human memory limitations and lexical competition. Lecture Notes in Computer Science, vol.
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