Implications of Using Internet Sting Corpora to Approximate Underage Victims

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 363–370 August 1–6, 2021. ©2021 Association for Computational Linguistics 363 LenAtten: An Effective Length Controlling Unit For Text Summarization Zhongyi Yu∗,1, Zhenghao Wu∗,1, Hao Zheng1, Zhe XuanYuan2,3, Jefferson Fong1, Weifeng Su†,1,3 1Computer Science and Technology Programme, 2Data Science Programme Division of Science and Technology 3Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College, Zhuhai, China {zhongyicst,zhecwu,zhslzwd97}@gmail.com {zhexuanyuan,jeffersonfong,wfsu}@uic.edu.cn Abstract Fixed length summarization aims at generat- ing summaries with a preset number of words or characters. Most recent researches incor- porate length information with word embed- dings as the input to the recurrent decoding unit, causing a compromise between length controllability and summary quality. In this work, we present an effective length control- ling unit Length Attention (LenAtten) to break this trade-off. Experimental results show that LenAtten not only brings improvements in length controllability and ROGUE scores but also has great generalization ability. In the task of generating a summary with the target length, our model is 732 times better than the best- performing length controllable summarizer in length controllability on the CNN/Daily Mail dataset. 1 1 Introduction Automatic text summarization aims at generating a short and coherent summary from one or multi- ple documents while preserving the main ideas of the original documents. Building upon the conven- tional summarization task, fixed length text summa- rization (FLS) demands extra focus on controlling the length of output summaries. Specifically, it re- quires generating summaries with a preset number of characters or words. FLS is a rising research topic required in many scenarios. For example, in order to get universal user experiences on multiple platforms and devices, titles and abstracts for news articles are expected to have different numbers of characters. Instead of manually rewriting summaries, FLS can auto- matically generate required summaries by simply ∗ Equal Contribution † Corresponding author 1Code are publicly available at: https://github. com/X-AISIG/LenAtten Source document egyptian president hosni mubarak arrived here friday morning to discuss the latest developments of iraqi crisis with his turkish counterpart suleyman demirel . Reference summary egyptian president to discuss iraqi crisis with turkish counterpart Model Summary PAULUS egyptian president arrives in ankara PAULUS+LA2 (GT) mubarak arrives in ankara for talks on iraqi crisis with turkish pm PAULUS+LA2 (30) egyptian president arrives in ankara PAULUS+LA2 (50) egyptian president arrives in ankara for talks on iraq crisis PAULUS+LA2 (70) mubarak arrives in ankara for talks on iraqi crisis with turkish president demirel Table 1: Output examples from the proposed method Length Attention (LA) on the Annotated English Giga- word dataset. Numbers in the parentheses represent dif- ferent desired lengths. (GT) means the desired length is equal to the number of characters in the reference summary. PAULUS (Paulus et al., 2018). inputting the desired output length. Besides, FLS can help news editors to reduce post-editing time (Makino et al., 2019) and further improve summary quality (Liu et al., 2018; Makino et al., 2019). Last but not least, as shown in Table 1, with …

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