{ "paper_id": "2019", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T07:26:58.632855Z" }, "title": "EBSUM: \u57fa\u65bc BERT \u7684\u5f37\u5065\u6027\u62bd\u53d6\u5f0f\u6458\u8981\u6cd5 EBSUM: An Enhanced BERT-based Extractive Summarization Framework", "authors": [ { "first": "\u5433\u653f\u80b2", "middle": [ "\uf02a" ], "last": "\u3001\u9673\u51a0\u5b87", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University of Science", "location": {} }, "email": "" }, { "first": "Zheng-Yu", "middle": [], "last": "Wu", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University of Science", "location": {} }, "email": "" }, { "first": "Kuan-Yu", "middle": [], "last": "Chen", "suffix": "", "affiliation": { "laboratory": "", "institution": "National Taiwan University of Science", "location": {} }, "email": "kychen@mail.ntust.edu.tw" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "Automatic summarization methods can be categorized into two major streams: the extractive summarization and the abstractive summarization. Although abstractive summarization is to generate a short paragraph for expressing the original document, but most of the generated summaries are hard to read. On the contrary, extractive summarization task is to extract sentences from the given document to", "pdf_parse": { "paper_id": "2019", "_pdf_hash": "", "abstract": [ { "text": "Automatic summarization methods can be categorized into two major streams: the extractive summarization and the abstractive summarization. Although abstractive summarization is to generate a short paragraph for expressing the original document, but most of the generated summaries are hard to read. On the contrary, extractive summarization task is to extract sentences from the given document to", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "\u96a8\u8457\u7db2\u969b\u7db2\u8def\u7684\u666e\u53ca\uff0c\u50b3\u7d71\u7684\u96fb\u5b50\u4f48\u544a\u6b04\u7cfb\u7d71\u8207\u65b0\u8208\u7684\u793e\u7fa4\u5a92\u9ad4\u3001\u6578\u4f4d\u8a0e\u8ad6\u5e73\u53f0\u65e5\u76ca\u84ec \u52c3\uff0c\u5927\u91cf\u7684\u6587\u5b57\u8cc7\u8a0a\u5145\u65a5\u5728\u7db2\u8def\u5a92\u9ad4\u4e0a\u3002\u6709\u8da3\u7684\u662f\uff0c\u96d6\u7136\u5b58\u5728\u5927\u91cf\u53ef\u5229\u7528\u7684\u6587\u5b57\u8cc7\u6599\uff0c \u4f46\u540c\u6642\u537b\u5b58\u5728\u8cc7\u8a0a\u6c3e\u6feb\u7684\u554f\u984c\u3002\u8cc7\u6599\u6aa2\u7d22(Information Retrieval)\u7684\u4efb\u52d9\u662f\u4f9d\u64da\u4f7f\u7528\u8005\u8f38\u5165 \u7684\u67e5\u8a62(Query)\uff0c\u5c0d\u6240\u6709\u6587\u7ae0\u9032\u884c\u6392\u5e8f\uff0c\u5f9e\u5927\u91cf\u7684\u8cc7\u8a0a\u4e2d\u64f7\u53d6\u4f7f\u7528\u8005\u9700\u8981\u7684\u6587\u7ae0 (Nogueira & Cho, 2019; Wu, Yen, & Chen, 2019; Yang, Zhang, & Lin, 2019) \u3002\u96d6\u7136\u8cc7\u8a0a\u6aa2\u7d22\u5df2\u80fd\u7be9\u9078 \u51fa\u4f7f\u7528\u8005\u6700\u53ef\u80fd\u9700\u8981\u7684\u6587\u7ae0\uff0c\u4f46\u6587\u7ae0\u6578\u91cf\u4ecd\u7136\u76f8\u7576\u9f90\u5927\u3001\u5167\u5bb9\u7e41\u96dc\u4e14\u5197\u9918\uff0c\u4f7f\u7528\u8005\u9700\u8981 \u8017\u8cbb\u591a\u9918\u7684\u6642\u9593\u8655\u7406\u4e0d\u5fc5\u8981\u7684\u8cc7\u8a0a\uff0c\u4ee5\u4fbf\u5f97\u77e5\u5404\u7bc7\u6587\u7ae0\u7684\u6709\u610f\u7fa9\u7684\u90e8\u5206\u3002\u70ba\u4e86\u9032\u4e00\u6b65\u5730 \u5e6b\u52a9\u4f7f\u7528\u8005\u8655\u7406\u5927\u91cf\u7684\u8cc7\u6599 (Pak & Paroubek, 2010 )\uff0c\u81ea\u52d5\u6587\u4ef6\u6458\u8981\u6280\u8853(F. Liu, Flanigan, Thomson, Sadeh, & Smith, 2018; Rambaut, Drummond, Xie, Baele, & Suchard, 2018) \u6210\u4e86\u4e00 \u500b\u91cd\u8981\u7684\u7814\u7a76\u8b70\u984c\uff0c\u4ed6\u4e0d\u50c5\u53ef\u4ee5\u5feb\u901f\u5730\u6574\u7406\u51fa\u6bcf\u4e00\u7bc7\u6587\u7ae0\u7684\u91cd\u9ede\u8cc7\u8a0a\uff0c\u8b93\u4f7f\u7528\u8005\u7be9\u9078\u81ea \u5df1\u6240\u9700\u8981\u7684\u8cc7\u8a0a\uff0c\u5728\u884c\u52d5\u88dd\u7f6e\u7684\u666e\u53ca\u7684\u4e16\u4ee3\u4e2d\uff0c\u5982\u4f55\u518d\u4f4e\u983b\u5bec\u4e0b\u5c07\u8cc7\u8a0a\u6574\u7406\uff0c\u5728\u6709\u9650\u7684 \u986f\u793a\u9762\u7a4d\u4e0b\uff0c\u5448\u73fe\u7d66\u4f7f\u7528\u8005\u66f4\u591a\u7684\u8cc7\u8a0a\uff0c\u4e5f\u6210\u4e86\u81ea\u52d5\u6458\u8981\u4e00\u500b\u5f88\u597d\u7684\u61c9\u7528\u5834\u666f (Billawala, Mehdad, Radev, Stent, & Thadani, 2018; Leiva, 2018) \u3002\u9664\u6b64\u4e4b\u5916\uff0c\u96fb\u5b50\u5a92\u9ad4\u7684\u767c\u5c55\uff0c\u70ba\u4e86 \u5546\u696d\u884c\u92b7\u3001\u5256\u6790\u6c11\u8ad6\u8f3f\u60c5\u6216\u662f\u8abf\u67e5\u793e\u6703\u610f\u5411\u7b49\uff0c\u4fc3\u4f7f\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u8207\u5206\u6790\u6210\u70ba\u91cd\u8981\u7684\u7814 \u7a76\u9818\u57df\u3002\u5176\u4e2d\uff0c\u6458\u8981\u7684\u62bd\u53d6\u8207\u5206\u6790\uff0c\u626e\u6f14\u8457\u5f88\u91cd\u8981\u7684\u89d2\u8272\u3002\u56e0\u6b64\uff0c\u6587\u4ef6\u6458\u8981\u4e0d\u50c5\u5e38\u88ab\u7528 \u65bc\u50b3\u7d71\u7684\u65b0\u805e\u5927\u7db1\u4efb\u52d9\u4e2d (Maskey & Hirschberg, 2003; Maybury & Merlino Jr, 2005 )\uff0c\u4ea6\u88ab \u61c9\u7528\u65bc\u8f3f\u60c5\u5206\u6790\u7684\u4efb\u52d9\u4e2d (Fan & Gordon, 2014; Imran, Castillo, Diaz, & Vieweg, 2015; Stieglitz & Dang-Xuan, 2013 )\u3002\u8f3f\u60c5\u7cfb\u7d71\u5fc5\u9808\u5c0d\u6587\u7ae0\u9032\u884c\u60c5\u7dd2\u5206\u6790\u5224\u65b7\u5176\u70ba\u6b63\u9762\u6216\u53cd\u9762\u610f \u898b (Fan & Gordon, 2014; He, Wu, Yan, Akula, & Shen, 2015 L. Liu et al., 2018; Nallapati, Zhou, dos Santos Gulcehre, & Xiang, 2016; See et al., 2017) \uff1b\u62bd\u53d6\u5f0f\u6458\u8981\u662f\u7531\u6587\u7ae0\u4e2d\u9078\u53d6\u5b8c\u6574\u7684\u53e5\u5b50 \u4f5c\u70ba\u6458\u8981\uff0c\u56e0\u6b64\u6458\u8981\u7684\u53ef\u8b80\u6027\u901a\u5e38\u662f\u8f03\u4f73\u7684\uff0c\u4e5f\u56e0\u6b64\uff0c\u62bd\u53d6\u5f0f\u6458\u8981\u6cd5\u4ecd\u7136\u662f\u8a31\u591a\u5b78\u8005\u7814 \u7a76\u7684\u8b70\u984c (Nallapati, Zhai, & Zhou, 2017; Narayan, Cohen & Lapate, 2018; Wong, Wu, & Li, 2008 ", "cite_spans": [ { "start": 170, "end": 192, "text": "(Nogueira & Cho, 2019;", "ref_id": "BIBREF33" }, { "start": 193, "end": 215, "text": "Wu, Yen, & Chen, 2019;", "ref_id": "BIBREF52" }, { "start": 216, "end": 241, "text": "Yang, Zhang, & Lin, 2019)", "ref_id": "BIBREF55" }, { "start": 345, "end": 366, "text": "(Pak & Paroubek, 2010", "ref_id": "BIBREF34" }, { "start": 381, "end": 426, "text": "Liu, Flanigan, Thomson, Sadeh, & Smith, 2018;", "ref_id": "BIBREF22" }, { "start": 427, "end": 474, "text": "Rambaut, Drummond, Xie, Baele, & Suchard, 2018)", "ref_id": "BIBREF43" }, { "start": 592, "end": 642, "text": "(Billawala, Mehdad, Radev, Stent, & Thadani, 2018;", "ref_id": "BIBREF2" }, { "start": 643, "end": 655, "text": "Leiva, 2018)", "ref_id": "BIBREF19" }, { "start": 763, "end": 790, "text": "(Maskey & Hirschberg, 2003;", "ref_id": "BIBREF26" }, { "start": 791, "end": 817, "text": "Maybury & Merlino Jr, 2005", "ref_id": "BIBREF27" }, { "start": 835, "end": 855, "text": "(Fan & Gordon, 2014;", "ref_id": "BIBREF8" }, { "start": 856, "end": 894, "text": "Imran, Castillo, Diaz, & Vieweg, 2015;", "ref_id": "BIBREF16" }, { "start": 895, "end": 922, "text": "Stieglitz & Dang-Xuan, 2013", "ref_id": "BIBREF45" }, { "start": 953, "end": 973, "text": "(Fan & Gordon, 2014;", "ref_id": "BIBREF8" }, { "start": 974, "end": 1006, "text": "He, Wu, Yan, Akula, & Shen, 2015", "ref_id": "BIBREF12" }, { "start": 1007, "end": 1027, "text": "L. Liu et al., 2018;", "ref_id": "BIBREF23" }, { "start": 1028, "end": 1080, "text": "Nallapati, Zhou, dos Santos Gulcehre, & Xiang, 2016;", "ref_id": "BIBREF31" }, { "start": 1081, "end": 1098, "text": "See et al., 2017)", "ref_id": "BIBREF44" }, { "start": 1162, "end": 1193, "text": "(Nallapati, Zhai, & Zhou, 2017;", "ref_id": "BIBREF30" }, { "start": 1194, "end": 1224, "text": "Narayan, Cohen & Lapate, 2018;", "ref_id": null }, { "start": 1225, "end": 1245, "text": "Wong, Wu, & Li, 2008", "ref_id": "BIBREF51" } ], "ref_spans": [], "eq_spans": [], "section": "\u7dd2\u8ad6 (Introduction)", "sec_num": "1." }, { "text": "BERTSUM \u5728\u62bd\u53d6\u5f0f\u6458\u8981\u4efb\u52d9\u4e2d\u53d6\u5f97\u76f8\u7576\u512a\u826f\u7684\u4efb\u52d9\u6210\u6548\uff0c\u4f46\u662f\u76e3\u7763\u5f0f\u5b78\u7fd2\u65b9\u5f0f\u5fc5\u9808\u4f9d \u8cf4\u5927\u91cf\u7684\u6a19\u8a18\u8a13\u7df4\u96c6\uff0c\u4e0d\u6613\u61c9\u7528\u65bc\u73fe\u5be6\u61c9\u7528\u4e2d (Cheng & Lapata, 2016; Gehrmann, Deng, & Rush, 2018; Nallapati et al., 2017; Nallapati et al., 2016; Narayan et al., 2018; Paulus, Xiong, & Socher, 2017; See et al., 2017) \uff0c\u56e0\u6b64\u7121\u76e3\u7763\u5b78\u7fd2\u4ecd\u7136\u662f\u8a31\u591a\u4efb\u52d9\u7684\u7814\u7a76\u65b9\u5411 (Erkan & Radev, 2004; Hirao, Yoshida, Nishino, Yasuda, & Nagata, 2013; Li, Wang, Lam, Ren, & Bing, 2017; Lin & Hovy, 2002; Marc, 1998; Mihalcea & Tarau, 2004; Parveen, Ramsl, & Strube, 2015; Radev, Jing & Budzikowska, 2000; Wan, 2008; Wan & Yang, 2008; Yin & Pei, 2015) ", "cite_spans": [ { "start": 63, "end": 85, "text": "(Cheng & Lapata, 2016;", "ref_id": "BIBREF5" }, { "start": 86, "end": 115, "text": "Gehrmann, Deng, & Rush, 2018;", "ref_id": "BIBREF9" }, { "start": 116, "end": 139, "text": "Nallapati et al., 2017;", "ref_id": "BIBREF30" }, { "start": 140, "end": 163, "text": "Nallapati et al., 2016;", "ref_id": "BIBREF31" }, { "start": 164, "end": 185, "text": "Narayan et al., 2018;", "ref_id": "BIBREF32" }, { "start": 186, "end": 216, "text": "Paulus, Xiong, & Socher, 2017;", "ref_id": "BIBREF36" }, { "start": 217, "end": 234, "text": "See et al., 2017)", "ref_id": "BIBREF44" }, { "start": 256, "end": 277, "text": "(Erkan & Radev, 2004;", "ref_id": "BIBREF7" }, { "start": 278, "end": 326, "text": "Hirao, Yoshida, Nishino, Yasuda, & Nagata, 2013;", "ref_id": "BIBREF14" }, { "start": 327, "end": 360, "text": "Li, Wang, Lam, Ren, & Bing, 2017;", "ref_id": "BIBREF20" }, { "start": 361, "end": 378, "text": "Lin & Hovy, 2002;", "ref_id": "BIBREF21" }, { "start": 379, "end": 390, "text": "Marc, 1998;", "ref_id": "BIBREF25" }, { "start": 391, "end": 414, "text": "Mihalcea & Tarau, 2004;", "ref_id": "BIBREF28" }, { "start": 415, "end": 446, "text": "Parveen, Ramsl, & Strube, 2015;", "ref_id": "BIBREF35" }, { "start": 447, "end": 479, "text": "Radev, Jing & Budzikowska, 2000;", "ref_id": "BIBREF41" }, { "start": 480, "end": 490, "text": "Wan, 2008;", "ref_id": "BIBREF48" }, { "start": 491, "end": 508, "text": "Wan & Yang, 2008;", "ref_id": "BIBREF49" }, { "start": 509, "end": 525, "text": "Yin & Pei, 2015)", "ref_id": "BIBREF56" } ], "ref_spans": [], "eq_spans": [], "section": "PACSUM (Zheng & Lapata, 2019a)", "sec_num": "3.2" }, { "text": "EQUATION", "cite_spans": [], "ref_spans": [], "eq_spans": [ { "start": 0, "end": 8, "text": "EQUATION", "ref_id": "EQREF", "raw_str": "\u3002 \u5927\u90e8\u5206\u7121\u76e3\u7763\u5f0f\u5b78\u7fd2\u7684\u62bd\u53d6\u5f0f\u6458\u8981\u4efb\u52d9\u63a1\u7528\u57fa\u65bc\u5716(Graph-based)\u7684\u6392\u5e8f\u6f14\u7b97\u6cd5\uff0c\u7528\u4ee5\u8a08 \u7b97\u53e5\u5b50\u5728\u6587\u7ae0\u4e2d\u7684\u986f\u8457\u6027(Salience)\u3002\u66f4\u660e\u78ba\u5730\uff0c\u7576\u5c07\u7d66\u5b9a\u6587\u7ae0 , , \u22ef , \uff0c \u53ef\u5c07\u6bcf\u500b\u53e5\u5b50 \u8868\u793a\u70ba\u5716\u4e2d\u7684\u7bc0\u9ede(node)\uff0c\u800c\u4efb\u4e8c\u500b\u7bc0\u9ede , \u9593\u6709\u908a \u76f8\u9023\uff0c \u4e26\u4e14\u4ee5\u76f8\u4f3c\u5ea6\u5206\u6578(similarity)\u4f5c\u70ba\u908a \u7684\u6b0a\u91cd\u3002\u63a5\u8457\uff0c\u518d\u4ee5\u5404\u5f0f\u5716\u8ad6\u7684\u6f14\u7b97\u6cd5\uff0c\u5982 Pagerank (Brin & Page, 1998)\uff0c\u8a08\u7b97\u6bcf\u4e00\u53e5\u5b50\u7684\u4e2d\u5fc3\u6027(centrality) : centrality \u2211 \u2208 ,\u2026, , ,\u2026,", "eq_num": "(4)" } ], "section": "PACSUM (Zheng & Lapata, 2019a)", "sec_num": "3.2" }, { "text": "\u6211\u5011\u4f7f\u7528 CNN/DailyMail (Hermann et al., 2015; See et al., 2017) ", "cite_spans": [ { "start": 19, "end": 41, "text": "(Hermann et al., 2015;", "ref_id": "BIBREF13" }, { "start": 42, "end": 59, "text": "See et al., 2017)", "ref_id": "BIBREF44" } ], "ref_spans": [], "eq_spans": [], "section": "\u5be6\u9a57\u7d50\u679c\u8207\u8a0e\u8ad6 (Experiment and Discussion)", "sec_num": "5." } ], "back_matter": [], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "This work is supported by the Ministry of Science and Technology (MOST) in Taiwan under grant MOST", "authors": [], "year": null, "venue": "and by the Project J367B83100 (ITRI) under the sponsorship of the Ministry of Economic Affairs", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "This work is supported by the Ministry of Science and Technology (MOST) in Taiwan under grant MOST 108-2636-E-011-005 (Young Scholar Fellowship Program), and by the Project J367B83100 (ITRI) under the sponsorship of the Ministry of Economic Affairs, Taiwan. \u53c3\u8003\u6587\u737b (References)", "links": null }, "BIBREF2": { "ref_id": "b2", "title": "Scalable and effective document summarization framework", "authors": [ { "first": "Y", "middle": [], "last": "Billawala", "suffix": "" }, { "first": "Y", "middle": [], "last": "Mehdad", "suffix": "" }, { "first": "D", "middle": [], "last": "Radev", "suffix": "" }, { "first": "A", "middle": [], "last": "Stent", "suffix": "" }, { "first": "K", "middle": [], "last": "Thadani", "suffix": "" } ], "year": 2018, "venue": "Google Patents", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Billawala, Y., Mehdad, Y., Radev, D., Stent, A., & Thadani, K. 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)\u3002 \u8fd1\u5e74\u4f86\uff0c\u7531\u65bc BERT \u6a21\u578b(Devlin, Chang, Lee, & Toutanova, 2018)\u7684\u63d0\u51fa\uff0c\u8a31\u591a\u81ea\u7136 \u8a9e\u8a00\u8655\u7406\u7684\u4efb\u52d9\u7686\u53d6\u5f97\u4e86\u7a81\u7834\u6027\u7684\u9032\u5c55\u3002\u5728\u62bd\u53d6\u5f0f\u6458\u8981\u7684\u7814\u7a76\u4e2d\uff0cBERTSUM(Y. Liu, 2019)\u4f7f\u7528 BERT \u53d6\u5f97\u6bcf\u500b\u53e5\u5b50\u7684\u8868\u793a\u6cd5\uff0c\u7136\u5f8c\u5229\u7528\u5f8c\u7e8c\u7684\u5206\u985e\u5668\uff0c\u70ba\u6bcf\u4e00\u500b\u53e5\u5b50\u8a55\u5206\uff0c \u4f5c\u70ba\u662f\u5426\u9078\u53d6\u53e5\u5b50\u7684\u4f9d\u64da\u3002\u6b64\u5916\uff0c\u70ba\u4e86\u6e1b\u5c11\u6458\u8981\u7684\u5197\u9918\uff0cBERTSUM \u63a1\u7528\u898f\u5247\u5f0f\u7684\u4e09\u9023 \u8a5e\u904e\u6ffe\u6cd5(Trigram Block)\uff0c\u6368\u68c4\u8207\u5df2\u9078\u6458\u8981\u5b58\u5728\u91cd\u8907\u4e09\u9023\u8a5e\u7684\u5019\u9078\u53e5\u5b50\uff0c\u85c9\u6b64\u6e1b\u5c11\u6458\u8981 \u4e2d\u5197\u9918\u7684\u8cc7\u8a0a\u3002\u96d6\u7136 BERTSUM \u5df2\u5728\u62bd\u53d6\u5f0f\u6458\u8981\u4efb\u52d9\u4e2d\u53d6\u5f97\u76f8\u7576\u512a\u826f\u7684\u4efb\u52d9\u6210\u6548\uff0c\u4f46\u6211 \u5011\u8a8d\u70ba\uff0cBERTSUM \u7f3a\u4e4f\u8003\u91cf\u53e5\u5b50\u5728\u6587\u7ae0\u4e2d\u7684\u4f4d\u7f6e\u8cc7\u8a0a\uff1b\u6b64\u5916\uff0cBERTSUM \u662f\u91dd\u5c0d\u6bcf\u4e00 \u500b\u53e5\u5b50\u4ee5\u8a08\u7b97\u4ea4\u53c9\u71b5(Cross Entropy)\u9032\u884c\u8a13\u7df4\uff0c\u76ee\u7684\u70ba\u6700\u5927\u5316\u8207\u6b63\u78ba\u6458\u8981\u53e5\u5b50\u7684\u4f3c\u7136\u503c (Likelihood)\uff0c\u4f46\u4ea4\u53c9\u71b5\u512a\u5316\u65b9\u5f0f\u4e0d\u9700\u8981\u5c0d\u53e5\u5b50\u9032\u884c\u6392\u540d\uff0c\u4e14\u8207\u6458\u8981\u7684\u8a55\u5206\u65b9\u6cd5 ROUGE \u4e4b\u9593\u4e0d\u5b58\u5728\u5c0d\u61c9\u7684\u95dc\u4fc2\uff1b\u9084\u6709\uff0cBERTSUM \u50c5\u4f7f\u7528\u7c21\u55ae\u7684\u4e09\u9023\u8a5e\u904e\u6ffe\u65b9\u6cd5\uff0c\u6e1b\u5c11\u5197\u9918\u8cc7 \u8a0a\u7684\u9078\u53d6\u3002\u6709\u9451\u65bc\u6b64\uff0c\u672c\u8ad6\u6587\u63d0\u51fa\u4e00\u5957\u57fa\u65bc BERT \u7684\u5f37\u5065\u6027\u6458\u8981\u65b9\u6cd5 EBSUM(Enhanced BERT-based Extractive Summarization Framework)\uff0c\u4ed6\u4e0d\u50c5\u8003\u91cf\u4e86\u53e5\u5b50\u5728\u6587\u7ae0\u4e2d\u7684\u4f4d\u7f6e\u8cc7 \u8a0a\uff0c\u5229\u7528\u5f37\u5316\u5b78\u7fd2(Narayan et al., 2018)\u7684\u65b9\u5f0f\u8b93\u6458\u8981\u6a21\u578b\u8207\u8a55\u4f30\u65b9\u5f0f\u7684\u95dc\u4fc2\u66f4\u70ba\u7dca\u5bc6\uff0c 22 \u5433\u653f\u80b2\u8207\u9673\u51a0\u5b87 2\u5206\u5e03\u5f0f\u5411\u91cf\u8868\u793a\u6cd5\u53c8\u7a31\u8a5e\u5411\u91cf(Word Embeddings)\uff0c\u662f\u5728\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u4e2d\u88ab\u5ee3\u6cdb\u4f7f\u7528\u7684\u65b9 \u6cd5\uff0c\u5176\u76ee\u7684\u662f\u5c07\u6bcf\u4e00\u500b\u55ae\u8a5e\u4ee5\u4e00\u500b\u4f4e\u7dad\u7a7a\u9593\u7684\u5206\u5e03\u5f0f\u5411\u91cf\u8868\u793a\u4e4b\u3002\u65e9\u671f\u7d93\u5178\u7684\u65b9\u6cd5\u6709\u9023 \u7e8c\u8a5e\u888b\u6a21\u578b(Continuous Bag-of-words, CBOW)\u3001\u8df3\u5b57\u6a21\u578b(Skip-gram) (Mikolov, Chen, Corrado, & Dean, 2013)\u4ee5\u53ca\u5168\u5c40\u5411\u91cf (Global Vectors, GloVe) (Pennington, Socher, & Manning, 2014)\u3002\u5728\u9019\u4e9b\u7d93\u5178\u7684\u6a21\u578b\u4e2d\uff0c\u6bcf\u4e00\u500b\u5b57\u8a5e\u5728\u4e0d\u540c\u7684\u4e0a\u4e0b\u6587\u4e2d\u662f\u4ee5\u76f8\u540c\u7684\u8a5e\u5411\u91cf \u8868\u793a\u4e4b\uff0c\u4f46\u8a31\u591a\u5b57\u8a5e\u5728\u4e0d\u540c\u7684\u4e0a\u4e0b\u6587\u4e2d\u6709\u8457\u4e0d\u540c\u7684\u542b\u610f\uff0c\u4f8b\u5982\"\u860b\u679c\"\u4e00\u8a5e\uff0c\u4f9d\u7167\u5176\u4e0a \u4e0b\u6587\uff0c\u53ef\u80fd\u4ee3\u8868\u8457\u624b\u6a5f\u54c1\u724c\u6216\u8005\u662f\u4e00\u7a2e\u6c34\u679c\u3002\u56e0\u6b64\uff0c\u70ba\u4e86\u8b93\u6bcf\u4e00\u500b\u8a5e\u7684\u8868\u793a\u6cd5\u66f4\u52a0\u5f37\u5065\uff0c 2018 \u5e74 Peter \u9996\u5148\u63d0\u51fa ELMo(Peters et al., 2018)\u67b6\u69cb\uff0c\u5229\u7528\u96d9\u5411\u9577\u77ed\u671f\u8a18\u61b6\u6a21\u578b(BiLSTM) \u8003\u616e\u4e0a\u4e0b\u6587\u7684\u8cc7\u8a0a\uff0c\u70ba\u6bcf\u4e00\u500b\u4f4d\u7f6e\u7684\u8a5e\u8f38\u51fa\u76f8\u5c0d\u61c9\u7684\u8a5e\u5411\u91cf\uff0c\u4e5f\u5c31\u662f\u5728\u4e0d\u540c\u4f4d\u7f6e\u7684\u540c\u4e00 \u500b\u8a5e\uff0c\u5c07\u6709\u8457\u4e0d\u540c\u7684\u4f4e\u7dad\u5ea6\u5411\u91cf\u8868\u793a\u6cd5\u3002\u63a5\u8457\uff0c\u7531\u65bc\u9577\u77ed\u671f\u8a18\u61b6\u6a21\u578b(Long Short-term Memory, LSTM)\u5728\u6a21\u578b\u53c3\u6578\u66f4\u65b0\u6642\uff0c\u7121\u6cd5\u4ee5\u5e73\u884c\u904b\u7b97\u7684\u65b9\u5f0f\u52a0\u901f\u8a08\u7b97\uff0c\u4e26\u4e14\u5728\u4e00\u4e9b\u4efb\u52d9 \u4e0a\u5df2\u88ab\u8b49\u660e Transformer(Vaswani et al., 2017)\u67b6\u69cb\u7684\u512a\u9ede\u8207\u6548\u80fd\uff0c\u56e0\u6b64 OpenAI \u57fa\u65bc Transformer \u67b6\u69cb\uff0c\u63d0\u51fa GPT(Generative Pre-training) (Radford, Narasimhan, Salimans, & Sutskever, 2018)\u6a21\u578b\uff0c\u7528\u65bc\u5b78\u7fd2\u6bcf\u4e00\u500b\u8a5e\u7684\u8a5e\u5411\u91cf\u8868\u793a\u6cd5\u3002\u96a8\u5f8c\uff0c\u57fa\u65bc GPT\uff0c\u8c37\u54e5\u53c8\u518d \u63d0 \u51fa \u539f\u672c\u7684\u6b63\u78ba\u55ae\u8a5e\uff1a \u4eca\u5929\u5929\u6c23\u771f \uff0c\u9069\u5408\u53bb \u91ce\u9910 (1) \u70ba\u4e86\u9632\u6b62\u6a21\u578b\u7121\u6cd5\u6536\u6582\uff0c\u5728\u5be6\u4f5c\u904e\u7a0b\u4e2d\uff0c80%\u7684\u8a13\u7df4\u6642\u9593\u662f\u4f7f\u7528[MASK]\u906e\u7f69\u55ae\u8a5e\uff0c \u53e6\u5916 10%\u7684\u8a13\u7df4\u6642\u9593\u5247\u4f7f\u7528\u4e00\u500b\u96a8\u6a5f\u7684\u8a5e\u7576\u4f5c\u906e\u7f69\uff0c\u4ee5\u53ca 10%\u7684\u8a13\u7df4\u6642\u9593\u7d66\u5b9a\u6b63\u78ba\u7684\u8a5e\u3002 \u7b2c\u4e8c\u6b65\u9a5f\uff0c\u70ba\u4e86\u4f7f BERT \u8003\u616e\u5230\u53e5\u5b50\u7b49\u7d1a\u95dc\u4fc2\uff0c\u56e0\u6b64\u9664\u4e86\u906e\u7f69\u8a9e\u8a00\u6a21\u578b\uff0c\u52a0\u5165\u53e5\u5b50\u95dc\u806f 23 [SEP]\u8868\u793a\u5206\u53e5\uff0c\u4e14\u5728\u9810\u8a13\u7df4\u4e2d\u6bcf\u500b\u4f4d\u7f6e\u7684\u5b57\u8a5e\u7531\u4e09\u7a2e\u5411\u91cf\u8868\u793a\u6cd5\u76f8\u52a0\u800c\u6210\uff0c\u5206\u5225\u662f\u4f4d\u7f6e \u5411\u91cf(Position Embedding)\u3001\u8a5e\u5411\u91cf(Token Embedding)\u4ee5\u53ca\u6bb5\u843d\u5411\u91cf(Segment Embedding)\u3002 \u4f4d\u7f6e\u5411\u91cf\u662f\u7528\u4f86\u8868\u793a\u9019\u500b\u5b57\u8a5e\u662f\u4f4d\u65bc\u8f38\u5165\u5e8f\u5217\u4e2d\u7684\u54ea\u4e00\u500b\u4f4d\u7f6e\uff1b\u6bb5\u843d\u5411\u91cf\u5247\u7528\u65bc\u8868\u793a\u55ae \u8a5e\u662f\u4f4d\u65bc\u4e0a\u53e5 E_A \u6216\u8005\u4e0b\u53e5 E_B\uff1b\u6700\u5f8c\uff0cBERT \u7684\u8f38\u5165\u70ba\u6b64\u4e09\u7a2e\u5411\u91cf\u76f8\u52a0\u5f62\u6210\uff0c\u800c[CLS] \u53ef\u4ee5\u8996\u70ba\u662f\u6574\u9ad4\u7684\u8868\u793a\u6cd5\uff0c\u4e26\u4e14\u88ab\u7528\u65bc\u53e5\u5b50\u95dc\u806f\u6027\u7684\u5206\u985e\u4efb\u52d9\u4e4b\u4e2d\u3002 3. \u57fa\u65bcBERT\u7684\u62bd\u53d6\u5f0f\u6458\u8981\u65b9\u6cd5 (BERT-Based Extractive Summarization Method) , \u22ef , (2) \u7531\u65bc , \u5433\u653f\u80b2\u8207\u9673\u51a0\u5b87 \u6210\u3002BERTSUM \u7684\u7279\u9ede\u662f\u5c07\u7b2c \u500b[CLS]\u6a19\u7c64\u5728 BERT \u4e2d\u6700\u5f8c\u4e00\u5c64\u7684\u8f38\u51fa\uff0c\u7576\u4f5c \u7684\u8868 \u793a\u6cd5 \u3002\u5728\u8a13\u7df4\u62bd\u53d6\u5f0f\u6458\u8981\u5668\u6642\uff0cBERTSUM \u5c07\u6bcf\u4e00\u500b\u53e5\u5b50 \u7531 BERT \u6240\u6c42\u5f97\u7684\u8868\u793a \u6cd5 \u7d93\u904e\u5206\u985e\u5668\u8f38\u51fa\u5206\u6578\uff0c\u5224\u65b7\u662f\u5426\u70ba\u6b63\u78ba\u7684\u6458\u8981\u53e5\u5b50\u3002\u82e5\u70ba\u6b63\u78ba\u7684\u6458\u8981\u53e5\u5b50\uff0c\u5176\u503c\u61c9 \u70ba 1\uff1b\u53cd\u4e4b\uff0c\u4e0d\u662f\u6458\u8981\u53e5\u5b50\u7684\u5206\u6578\u61c9\u70ba 0 : 0 1 (3) BERTSUM \u63d0\u51fa\u7684\u4e09\u7a2e\u5206\u985e\u5668\uff0c\u5305\u62ec\u7c21\u55ae\u5206\u985e\u5668(Simple Classifier)\u3001Transformer \u4ee5\u53ca\u905e \u8ff4\u795e\u7d93\u7db2\u8def(Recurrent Neural Network, RNN) (Hochreiter & Schmidhuber, 1997)\uff0c\u4e26\u4e14\u5229\u7528 \u4e8c\u5206\u985e\u4ea4\u53c9\u71b5(Binary Cross Entropy)\u8a08\u7b97\u9810\u6e2c\u8aa4\u5dee\uff0c\u4e26\u4f9d\u6b64\u70ba\u6574\u500b BERTSUM \u6a21\u578b\u9032\u884c \u53c3\u6578\u7684\u8a13\u7df4\u8207\u8abf\u6574\u3002BERTSUM \u7684\u6a21\u578b\u67b6\u69cb\u5982\u5716 2 \u6240\u793a\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u5728\u6e2c\u8a66\u968e\u6bb5(Test Stage)\uff0c\u70ba\u4e86\u589e\u52a0\u6458\u8981\u5167\u5bb9\u7684\u591a\u6a23\u6027\u3001\u907f\u514d\u9078\u53d6\u51fa\u5197\u9918\u7684\u53e5\u5b50\uff0cBEETSUM \u4f7f\u7528\u4e09\u9023\u8a5e\u904e \u6ffe\u65b9\u6cd5(Carbonell & Goldstein, 1998)\uff0c\u4ee5\u6e1b\u5c11\u6458\u8981\u7684\u5197\u9918\uff0c\u7576\u7d66\u5b9a\u5df2\u9078\u6458\u8981\u53e5\u5b50\u96c6 \u4ee5\u53ca BERTSUM \u6240\u9810\u6e2c\u51fa\u7684\u5019\u9078\u6458\u8981\u53e5\u5b50c\uff0c\u82e5c\u8207 \u5b58\u5728\u4efb\u4e00\u4e09\u5b57\u5143\u7d44\uff0c\u5247\u5ffd\u7565c\uff0c\u4f7f\u5f97\u6458\u8981 \u6027\u7684\u5206\u985e\u4efb\u52d9(EBSUM: \u57fa\u65bcBERT\u7684\u5f37\u5065\u6027\u62bd\u53d6\u5f0f\u6458\u8981\u6cd5 \u4e2d\u6bcf\u4e00\u500b\u53e5\u5b50\u4e0d\u76f8\u4e92\u91cd\u758a\u3002
\u4e5f\u9032\u4e00\u6b65\u5730\u8b93\u6458\u8981\u6a21\u578b\u672c\u8eab\u5177\u5099\u6e1b\u5c11\u5197\u9918\u8cc7\u8a0a\u9078\u53d6\u7684\u80fd\u529b\u3002 \u5176\u4e2d\uff0c \u8868\u793a\u5728 \u4e2d\u7b2c \u500b\u53e5\u5b50\u3002\u5728\u8f38\u5165\u7684\u968e\u6bb5\uff0cBERTSUM \u5728\u6bcf\u4e00\u500b\u53e5\u5b50\u7684\u958b\u982d\u52a0\u5165
[CLS]\uff0c\u4e26\u4e14\u5728\u53e5\u5b50\u7d50\u5c3e\u52a0\u5165[SEP]\uff1b\u53e6\u5916\uff0c\u8207 BERT \u539f\u8ad6\u6587\u76f8\u540c\uff0c\u6bcf\u4e00\u500b\u53e5\u5b50\u7686\u6709\u4e00\u500b
\u6bb5\u843d\u5411\u91cf\uff1b\u6700\u5f8c\uff0c\u6bcf\u4e00\u500b\u5b57\u7684\u5411\u91cf\u8868\u793a\u6cd5\u5373\u662f\u5c07\u4f4d\u7f6e\u5411\u91cf\u3001\u8a5e\u5411\u91cf\u4ee5\u53ca\u6bb5\u843d\u5411\u91cf\u76f8\u52a0\u800c
" }, "TABREF2": { "html": null, "num": null, "type_str": "table", "text": "", "content": "
EBSUM: \u57fa\u65bcBERT\u7684\u5f37\u5065\u6027\u62bd\u53d6\u5f0f\u6458\u8981\u6cd525 \u5433\u653f\u80b2\u8207\u9673\u51a0\u5b87
1\uff0c\u5728\u5be6\u9a57\u4e2d PACSUM \u767c\u73fe \u4f46\u4ea4\u53c9\u71b5\u512a\u5316\u65b9\u5f0f\u4e0d\u9700\u8981\u5c0d\u53e5\u5b50\u9032\u884c\u6392\u540d\uff0c\u4e14\u8207\u6458\u8981\u7684\u8a55\u5206\u65b9\u6cd5 ROUGE \u4e4b\u9593\u4e0d\u5b58\u5728\u5c0d 0\u80fd\u5920\u6709\u6700\u4f73\u7684\u6548\u679c\uff0c\u9019\u8868\u660e\u4e86\u82e5\u53e5\u5b50\u8207\u524d\u9762 \u7684\u6458\u8981\u53e5\u5b50\u53ef\u4ee5\u7372\u5f97\u8f03\u9ad8\u7684\u9810\u6e2c\u4f3c\u7136\u503c(Likelihood)\uff0c\u4f46\u4ee5\u512a\u5316\u6bcf\u4e00\u500b\u53e5\u5b50\u7684\u4ea4\u53c9\u71b5\u503c\u70ba
\u7684\u53e5\u5b50\u76f8\u4f3c\u7684\u8a71\uff0c\u6703\u4f7f\u4e2d\u5fc3\u6027\u5206\u6578\u964d\u4f4e\u3002\u9664\u4e86\u5206\u6578\u8a08\u7b97\u7684\u65b9\u5f0f\u6539\u8b8a\u5916\uff0cPACSUM \u4f7f\u7528 \u61c9\u7684\u95dc\u4fc2\uff1b\u9084\u6709\uff0cBERTSUM \u4f7f\u7528\u4e09\u9023\u8a5e\u904e\u6ffe\u65b9\u6cd5\uff0c\u6e1b\u5c11\u5197\u9918\u8cc7\u8a0a\u7684\u9078\u53d6\u3002\u6709\u9451\u65bc\u6b64\uff0c \u76ee\u6a19\uff0c\u6703\u5ffd\u7565\u53e5\u5b50\u8207\u53e5\u5b50\u4e4b\u9593\u7684\u6392\u540d\u95dc\u4fc2\uff0c\u4e26\u4e14\u9019\u6a23\u7684\u8a13\u7df4\u65b9\u5f0f\u4e26\u7121\u6cd5\u76f4\u63a5\u8207\u6458\u8981\u7684\u5206
\u6b64\u4e00\u4e2d\u5fc3\u6027\u5206\u6578\u5c31\u88ab\u7528\u4f86\u505a\u70ba\u8a72\u53e5\u5b50\u662f\u5426\u70ba\u6458\u8981\u53e5\u5b50\u7684\u5206\u6578\u3002 PACSUM \u8a8d\u70ba Pagerank \u4e26\u7121\u8003\u616e\u5230\u5716\u7684\u65b9\u5411\u6027(Undirected)\uff0c\u56e0\u6b64 PACSUM \u900f\u904e\u76f8 \u5c0d\u4f4d\u7f6e\u4f86\u8a08\u7b97\u4e2d\u5fc3\u6027\uff0c\u5e0c\u671b\u8d8a\u65e9\u51fa\u73fe\u7684\u53e5\u5b50\u8d8a\u91cd\u8981\u3002\u57fa\u65bc\u9019\u500b\u60f3\u6cd5\uff0cPACSUM \u8a08\u7b97 \u4e2d BERT \u505a \u70ba \u53e5 \u5b50 \u7684 \u7de8 \u78bc \u5668 \uff0c \u4e26 \u4ee5 \u53e5 \u5b50 \u5c64 \u7d1a \u7684 \u5206 \u4f48 \u5047 \u8a2d (Sentence-level distributional hypothesis) (Harris, 1954; Polajnar, Rimell, & Clark, 2015)\u9032\u884c BERT \u53c3\u6578\u7684\u5fae\u8abf\uff1a ~ (6) \u5176\u4e2d \u4ee5\u53ca \u70ba\u5169\u500b\u4e0d\u540c\u7684 BERT\uff0c \u70ba\u6fc0\u6d3b\u51fd\u6578 sigmoid\uff0c \u70ba\u5747\u52fb\u5206\u4f48\u7684\u53e5\u5b50 \u7a7a\u9593\u3002\u6a21\u578b\u8a13\u7df4\u5b8c\u6210\u5f8c\uff0c\u53ef\u7528\u65bc\u7522\u751f\u53e5\u5b50\u7684\u5411\u91cf\u8868\u793a\u6cd5\u3002 4. EBSUM\uff1a\u57fa\u65bcBERT\u7684\u5f37\u5065\u6027\u62bd\u53d6\u5f0f\u6458\u8981\u65b9\u6cd5 (Enhanced BERT-based Extractive Summarization Framework, EBSUM) \u5716 3. \u57fa\u65bc BERT \u7684\u5f37\u5065\u6027\u62bd\u53d6\u5f0f\u6458\u8981\u6cd5(EBSUM)\u67b6\u69cb\u5716\u3002 [Fiqure 3. Illustration of EBSUM Model.] \u672c\u8ad6\u6587\u63d0\u51fa\u4e00\u5957\u57fa\u65bc BERT \u7684\u9032\u968e\u7248\u6458\u8981\u65b9\u6cd5 EBSUM(Enhanced BERT-based Extractive Summarization Framework)\uff0c\u4ed6\u4e0d\u50c5\u8003\u91cf\u4e86\u53e5\u5b50\u5728\u6587\u7ae0\u4e2d\u7684\u4f4d\u7f6e\u8cc7\u8a0a\uff0c\u5229\u7528\u5f37\u5316\u5b78\u7fd2\u7684\u65b9 \u5f0f\u8b93\u6458\u8981\u6a21\u578b\u8207\u8a55\u4f30\u65b9\u5f0f\u7684\u95dc\u4fc2\u66f4\u70ba\u7dca\u5bc6\uff0c\u4e5f\u9032\u4e00\u6b65\u5730\u8b93\u6458\u8981\u6a21\u578b\u672c\u8eab\u5177\u5099\u6e1b\u5c11\u5197\u9918\u8cc7 \u8a0a\u9078\u53d6\u7684\u80fd\u529b\u3002 \u6211\u5011\u6240\u63d0\u51fa\u4e4b EBSUM \u6458\u8981\u6a21\u578b\u67b6\u69cb\u5982\u5716 3 \u6240\u793a\u3002\u9996\u5148\uff0c\u5728\u8f38\u5165\u7684\u968e\u6bb5\uff0cEBSUM \u5c07\u6587\u7ae0 \u8996\u70ba\u4e00\u9023\u4e32\u53e5\u5b50\u7684\u96c6\u5408 , , \u22ef , \uff0c \u8868\u793a\u6587\u7ae0 \u4e2d\u7b2c \u500b\u53e5 \u5b50\uff0c\u4e26\u4e14\u5728\u6bcf\u4e00\u500b\u53e5\u5b50\u7684\u958b\u982d\u52a0\u5165[CLS]\u3001\u53e5\u5b50\u7d50\u5c3e\u52a0\u5165[SEP]\u3002 4.1 \u53e5\u5b50\u4f4d\u7f6e\u5411\u91cf (Sentence Position Embedding) \u8a31\u591a\u7814\u7a76\u6307\u51fa\u4eba\u5011\u5728\u64b0\u5beb\u6587\u7ae0\u6642\uff0c\u6703\u7fd2\u6163\u5728\u6587\u7ae0\u524d\u534a\u90e8\u8b1b\u8ff0\u91cd\u9ede\uff0c\u5c24\u5176\u65b0\u805e\u6587\u7ae0\u66f4\u662f\u5e38 \u898b\uff0c\u56e0\u6b64\u6709\u8a31\u591a\u81ea\u52d5\u6458\u8981\u6a21\u578b\uff0c\u6703\u5c07\u53e5\u5b50\u5728\u6587\u7ae0\u4e2d\u7684\u4f4d\u7f6e\u8cc7\u8a0a\u4f5c\u70ba\u4e00\u9805\u7279\u5fb5\u3002\u70ba\u4e86\u5c07\u6b64 \u4e00\u7279\u5fb5\u7d0d\u5165\u904b\u7528\uff0cEBSUM \u63d0\u51fa\u4e09\u7a2e\u8003\u616e\u53e5\u5b50\u4f4d\u7f6e\u8cc7\u8a0a\u7684\u7279\u5fb5\uff1aCLS \u5411\u91cf\u3001ALL \u5411\u91cf\u4ee5 \u53ca SEP \u5411\u91cf\uff0c\u5982\u5716 4 \u6240\u793a\u3002CLS \u5411\u91cf\u662f\u5728\u6bcf\u4e00\u53e5 \u7684\u958b\u982d\u7b26\u865f[CLS]\u4f4d\u7f6e\u4e0a\uff0c\u52a0\u4e0a\u5c0d \u61c9\u8a72\u53e5\u7684\u4f4d\u7f6e\u5411\u91cf \uff0c\u5176\u4e2d \u70ba\u53e5\u5b50 \u5728\u6587\u7ae0\u7684\u4f4d\u7f6e\uff0c\u800c\u9664\u4e86[CLS]\u4ee5\u5916\u7684\u5b57\u8a5e\uff0c\u901a\u901a \u52a0\u4e0a\u4e00\u500b\u76f8\u540c\u7684\u7279\u5fb5 \uff1bALL \u5411\u91cf\u5247\u662f\u5c07\u6bcf\u4e00\u500b\u53e5\u5b50 \u4e2d\u7684\u6bcf\u4e00\u500b\u5b57\u8a5e\u90fd\u52a0\u5165\u4ee3\u8868 \u9019\u4e00\u500b\u53e5\u5b50\u7684\u4f4d\u7f6e\u5411\u91cf \uff1b\u8207 CLS \u5411\u91cf\u76f8\u53cd\uff0cSEP \u5411\u91cf\u662f\u5728\u6bcf\u4e00\u53e5 \u7684\u7d50\u5c3e\u7b26\u865f [SEP] \u4f4d\u7f6e\u4e0a\uff0c\u52a0\u4e0a\u5c0d\u61c9\u8a72\u53e5\u7684\u4f4d\u7f6e\u5411\u91cf \uff0c\u5176\u4e2d \u70ba\u53e5\u5b50 \u5728\u6587\u7ae0\u7684\u4f4d\u7f6e\uff0c\u800c\u9664\u4e86[SEP]\u4ee5 \u5916\u7684\u5b57\u8a5e\uff0c\u901a\u901a\u52a0\u4e0a\u4e00\u500b\u76f8\u540c\u7684\u7279\u5fb5 \u3002 \u5716 4. \u4e09\u7a2e\u53e5\u5b50\u4f4d\u7f6e\u5411\u91cf\uff0c\u5206\u5225\u70ba SEP \u5411\u91cf\u3001CLS \u5411\u91cf\u3001ALL \u5411\u91cf\u3002 [Fiqure 4. SEP Embeddings\u3001CLS Embeddings and ALL Embeddings.] 4.2 \u5f37\u5316\u5b78\u7fd2(Reinforcement Learning) \u6578\u76f8\u5c0d\u61c9\u3002\u6240\u4ee5\uff0c\u6709\u7814\u7a76\u6307\u51fa\uff0c\u4f7f\u7528\u4ea4\u53c9\u71b5\u4f5c\u70ba\u512a\u5316\u7684\u76ee\u6a19\u5bb9\u6613\u7522\u751f\u904e\u9577\u7684\u6458\u8981\u6216\u8005\u9078 \u53d6\u5230\u5197\u9918\u7684\u53e5\u5b50(Narayan et al., 2018)\u3002\u6709\u9451\u65bc\u6b64\uff0c\u672c\u7814\u7a76\u900f\u904e\u5f37\u5316\u5b78\u7fd2(Sutton & Barto, 2018)\u4e2d\u7684\u7b56\u7565\u5b78\u7fd2(Policy Learning) (Williams, 1992)\uff0c\u5e0c\u671b\u8b93\u6a21\u578b\u7684\u8a13\u7df4\u76ee\u6a19\u8207\u6458\u8981\u5206\u6578 (\u5373 ROUGE)\u6709\u66f4\u660e\u78ba\u7684\u5c0d\u61c9\u95dc\u4fc2\u3002\u5728\u5f37\u5316\u5b78\u7fd2\u4e2d\uff0c\u5c0d\u65bc\u7d66\u5b9a\u7684\u4e00\u7bc7\u6587\u7ae0 \uff0c\u6211\u5011\u53ef\u4ee5 \u96a8\u6a5f\u7684\u62bd\u53d6\u4e00\u7d44\u53e5\u5b50 \u5f62\u6210\u6458\u8981\uff0c\u4e26\u4ee5 ROUGE-1 \u8207 ROUGE-2 \u7684 F1 \u5206\u6578\u7e3d\u548c\u505a\u70ba\u9019\u7d44 \u53e5\u5b50\u5c0d\u61c9\u7684\u734e\u52f5\u503c \u3002\u56e0\u6b64\uff0c\u5f37\u5316\u5b78\u7fd2\u7684\u76ee\u6a19\u51fd\u5f0f\u70ba\u6700\u5c0f\u5316\u9810\u671f\u8ca0\u9762\u734e\u52f5\u503c(Negative Expected Reward)\uff1a \u0398 ~ \u2022| , (7) \u5176\u4e2d\u0398\u662f\u62bd\u53d6\u5f0f\u6458\u8981\u6a21\u578b\u53c3\u6578\u3002\u7576\u4f7f\u7528\u5f37\u5316\u5b78\u7fd2\u65bc\u62bd\u53d6\u5f0f\u6458\u8981\u7684\u4efb\u52d9\u4e0a\u6642\uff0c\u5be6\u4f5c\u4e0a\u6211\u5011 \u5728\u5176\u4ea4\u53c9\u71b5\u640d\u5931\u4e0a\u8207 \u76f8\u4e58\uff0c\u5176\u76ee\u6a19\u662f\u8b93\u6a21\u578b\u61c2\u5f97\u5340\u5225\u54ea\u4e9b\u53e5\u5b50\u5bb9\u6613\u51fa\u73fe\u5728\u9ad8\u734e\u52f5\u503c \u7684\u7d44\u5408\u4e2d\uff0c\u54ea\u4e9b\u53e5\u5b50\u5247\u901a\u5e38\u6703\u8b93\u734e\u52f5\u503c\u8b8a\u4f4e\uff0c\u56e0\u6b64\u9054\u5230\u62bd\u53d6\u5f0f\u6458\u8981\u6a21\u578b\u61c2\u5f97\u5982\u4f55\u62bd\u53d6\u9069 \u7576\u7684\u53e5\u5b50\u7d44\u6210\u6458\u8981\u3002 4.3 \u70ba\u4e86\u4f7f\u62bd\u53d6\u5f0f\u6458\u8981\u6a21\u578b\u53ef\u4ee5\u81ea\u52d5\u5730\u8003\u91cf\u5197\u9918\u8cc7\u8a0a\u7684\u554f\u984c\uff0c\u6211\u5011\u767c\u60f3\u65bc\u8cc7\u8a0a\u6aa2\u7d22\u9818\u57df\u4e2d\u7684 \u6700\u5927\u908a\u7de3\u76f8\u95dc\u6027(Maximal Marginal Relevance, MMR)\u6e96\u5247\uff0c\u63d0\u51fa\u6700\u5927\u908a\u7de3\u76f8\u95dc\u6027\u5411\u91cf\uff0c\u4f7f \u5f97 EBSUM \u5728\u6bcf\u500b\u4e16\u4ee3(iteration)\u90fd\u53ef\u4ee5\u9078\u64c7\u51fa\u65b0\u7a4e\u4e14\u5bcc\u6709\u8cc7\u8a0a\u7684\u53e5\u5b50\u505a\u70ba\u6458\u8981\u3002\u66f4\u660e\u78ba \u5730\uff0c\u7576\u7d66\u5b9a\u6458\u8981\u6587\u7ae0 , , \u22ef , \u4ee5\u53ca\u5df2\u9078\u53d6\u7684\u6458\u8981\u96c6S \uff0c\u5c0d\u65bc\u6587\u7ae0\u4e2d\u5df2\u51fa \u73fe\u5728S \u5167\u7684\u5b57\u8a5e\uff0c\u6211\u5011\u7d66\u4e88\u4e00\u500b\u6700\u5927\u908a\u7de3\u76f8\u95dc\u6027\u5411\u91cf \uff1b\u53cd\u4e4b\uff0c\u6587\u7ae0\u4e2d\u672a\u5728S \u4e2d\u51fa\u73fe\u7684\u5b57 \u8a5e\uff0c\u4ee5\u4e00\u500b\u6700\u5927\u908a\u7de3\u76f8\u95dc\u6027\u5411\u91cf \u8868\u793a\u4e4b\uff0c\u5176\u521d\u59cb\u503c\u70ba\u96a8\u6a5f\u5411\u91cf\uff0c\u4e26\u4ea4\u7531 BERT \u8a13\u7df4\u5b78 \u7fd2\uff0c\u5982\u5716 5 \u6240\u793a\u3002\u70ba\u4e86\u8b93 EBSUM \u78ba\u5be6\u4e86\u89e3\u6700\u5927\u908a\u7de3\u76f8\u95dc\u6027\u5411\u91cf\u7684\u610f\u7fa9\uff0c\u5728\u8a13\u7df4\u6642\uff0c\u6211 \u5011\u5c07\u8a13\u7df4\u8cc7\u6599\u4e2d\u7684\u6bcf\u4e00\u7bc7\u6587\u7ae0\u96a8\u6a5f\u7684\u6311\u9078 0~3 \u53e5\u6b63\u78ba\u6458\u8981\u53e5\u5b50\u653e\u5165\u96c6\u5408S \u4e2d\uff0c\u6a21\u578b\u53c3\u6578\u7684 \u66f4\u65b0\u76ee\u6a19\u5247\u662f\u6b63\u78ba\u5730\u9078\u53d6\u5269\u9918\u7684\u6b63\u78ba\u6458\u8981\u53e5\u5b50\u3002 \u5716 5. \u6700\u5927\u908a\u7de3\u76f8\u95dc\u6027\u5411\u91cf\u3002 \u4f4d\u7f6e\u5411\u91cf\u3001\u8a5e\u5411\u91cf\u4ee5\u53ca\u6bb5\u843d\u5411\u91cf\u5916\uff0c\u6211\u5011\u984d\u5916\u5f15\u5165\u4e86\u53e5\u5b50\u4f4d\u7f6e\u5411\u91cf\u4ee5\u53ca\u6700\u5927\u908a\u7de3\u76f8\u95dc\u6027 \u5411\u91cf\uff0c\u6240\u4ee5\u5728 EBSUM \u4e2d\uff0c\u6bcf\u4e00\u500b\u5b57\u7684\u5411\u91cf\u8868\u793a\u6cd5\u5373\u662f\u5c07\u9019\u4e9b\u5411\u91cf\u76f8\u52a0\u800c\u6210\u3002\u63a5\u8457\uff0c\u85c9 \u7531 BERT\uff0c\u6211\u5011\u53ef\u4ee5\u5f97\u5230\u6bcf\u4e00\u500b\u53e5\u5b50\u7684\u5411\u91cf\u8868\u793a\u6cd5\uff0c\u5373\u7b2c \u500b[CLS]\u6a19\u7c64\u5728 BERT \u4e2d\u6700\u5f8c \u4e00\u5c64\u7684\u8f38\u51fa\uff0c\u7576\u4f5c \u7684\u8868\u793a\u6cd5 \u3002\u5728\u7372\u5f97\u6bcf\u4e00\u500b\u53e5\u5b50\u7684\u8868\u793a\u6cd5\u5f8c\uff0c\u6211\u5011\u5c07\u9019\u4e9b\u5411\u91cf\u8f38 \u5165\u7531\u591a\u5c64 Transformer \u5806\u758a\u800c\u6210\u7684\u6458\u8981\u4efb\u52d9(Summarization Layer)\u5c64(Ba, Kiros, & Hinton, 2016; Vaswani et al., 2017)\uff0c\u6700\u7d42\u8f38\u51fa\u6bcf\u500b\u53e5\u5b50\u88ab\u9078\u64c7\u70ba\u6458\u8981\u7684\u6a5f\u7387\u3002\u503c\u5f97\u4e00\u63d0\u7684\u662f\uff0c\u7531 \u65bc\u6700\u5927\u908a\u7de3\u76f8\u95dc\u6027\u5411\u91cf\u7684\u52a0\u5165\uff0cEBSUM \u662f\u905e\u8ff4\u5f0f\u7684\u6bcf\u6b21\u9078\u53d6\u8a72\u6b21\u9810\u6e2c\u5206\u6578\u6700\u5927\u7684\u53e5\u5b50 \u52a0\u5165\u5df2\u9078\u6458\u8981\u96c6S \u4e2d\uff0c\u76f4\u5230\u53e5\u5b50\u6578(\u6216\u9810\u5148\u8a2d\u5b9a\u7684\u6458\u8981\u6bd4\u4f8b\u6216\u5b57\u6578)\u9054\u5230\u9810\u5148\u8a2d\u5b9a\u7684\u6578\u91cf [Fiqure 5\u70ba\u4e86\u8b93 EBSUM \u8003\u91cf\u53e5\u5b50\u5728\u6587\u7ae0\u4e2d\u7684\u4f4d\u7f6e\u8cc7\u8a0a\u4ee5\u53ca\u5177\u5099\u62b5\u6297\u5197\u9918\u8cc7\u8a0a\u7684\u80fd\u529b\uff0c\u56e0\u6b64\u9664\u4e86 \u5f8c\uff0c\u6574\u500b\u904e\u7a0b\u5373\u505c\u6b62\u3002
\u5fc3\u6027\u5206\u6578\u7684\u65b9\u5f0f\u70ba\uff1a centrality \u96d6\u7136 BERTSUM \u5df2\u5728\u62bd\u53d6\u5f0f\u6458\u8981\u4efb\u52d9\u4e2d\u53d6\u5f97\u76f8\u7576\u512a\u826f\u7684\u4efb\u52d9\u6210\u6548\uff0c\u4f46\u6211\u5011\u8a8d\u70ba\uff0c \u2211 \u5728\u62bd\u53d6\u5f0f\u6458\u8981\u4efb\u52d9\u4e2d\uff0c\u8a31\u591a\u6a21\u578b\u5229\u7528\u795e\u7d93\u7db2\u8def\u5c0d\u6587\u7ae0\u4e2d\u7684\u6bcf\u4e00\u500b\u53e5\u5b50\u9032\u884c\u5404\u5f0f\u7279\u5fb5\u62bd\u53d6\uff0c \u2211 (5) BERTSUM \u7f3a\u4e4f\u8003\u91cf\u53e5\u5b50\u5728\u6587\u7ae0\u4e2d\u7684\u4f4d\u7f6e\u8cc7\u8a0a\uff1b\u6b64\u5916\uff0cBERTSUM \u662f\u91dd\u5c0d\u6bcf\u4e00\u500b\u53e5\u5b50\u4ee5 \u85c9\u7531\u9019\u4e9b\u7279\u5fb5\uff0c\u9810\u6e2c\u6bcf\u500b\u53e5\u5b50\u88ab\u9078\u70ba\u6458\u8981\u53e5\u7684\u5206\u6578\uff0c\u4e26\u4f7f\u7528\u4ea4\u53c9\u71b5(Cross Entropy)\u8a08\u7b97\u7576
\u5176\u4e2d\uff0c \u4ee5\u53ca \u5206\u5225\u70ba\u524d\u5411(Forward-looking)\u4ee5\u53ca\u5f8c\u5411(Backward-looking)\u7684\u6b0a\u91cd\uff0c\u4e14 \u8a08\u7b97\u4ea4\u53c9\u71b5(Cross Entropy)\u9032\u884c\u8a13\u7df4\uff0c\u76ee\u7684\u70ba\u6700\u5927\u5316\u8207\u6b63\u78ba\u6458\u8981\u53e5\u5b50\u7684\u4f3c\u7136(Likelihood)\uff0c \u524d\u6a21\u578b\u7684\u9810\u6e2c\u7d50\u679c\u8207\u6b63\u78ba\u7b54\u6848\u7684\u5dee\u7570\uff0c\u518d\u9032\u884c\u6a21\u578b\u53c3\u6578\u7684\u66f4\u65b0\u3002\u9019\u6a23\u7684\u505a\u6cd5\u662f\u5e0c\u671b\u6b63\u78ba
" }, "TABREF3": { "html": null, "num": null, "type_str": "table", "text": "Trigram Block\"\uff0c\u5206\u5225\u52a0\u5165 CLS \u5411\u91cf\u3001SEP \u5411\u91cf\u4ee5\u53ca ALL \u5411\u91cf\uff0c\u5be6\u9a57\u7d50 \u679c\u5982\u8868 4 \u6240\u793a\uff0c\u5206\u5225\u8868\u793a\u70ba \"BERTSUM-Trigram Block+CLS\"\u3001 \"BERTSUM-Trigram Block+SEP\"\u8207\"BERTSUM-Trigram Block+ALL\"\u3002\u7531\u5be6\u9a57\u7d50\u679c\u53ef\u77e5\uff0c\u5c07\u53e5\u5b50\u4f4d\u7f6e\u7279\u5fb5\u52a0\u5165 \u6240\u6709\u5b57\u8a5e\u7576\u4e2d\u7684\u8868\u73fe\u6700\u5dee(\u5373 ALL \u5411\u91cf)\uff0c\u9019\u53ef\u80fd\u662f\u56e0\u70ba\u5c07\u53e5\u5b50\u4f4d\u7f6e\u8cc7\u8a0a\u52a0\u5165\u6240\u6709\u5b57\u8a5e \u5411\u91cf\u4e2d\uff0c\u4f7f\u5f97\u5b57\u8a5e\u5411\u91cf\u8868\u793a\u6cd5\u904e\u65bc\u542b\u7cca\uff0c\u56e0\u6b64\u82e5\u50c5\u5c07\u53e5\u5b50\u4f4d\u7f6e\u8cc7\u8a0a\u52a0\u5165[CLS]\u6216[SEP]\u4e2d\uff0c \u8f03\u4e0d\u6703\u5f71\u97ff\u5b57\u8a5e\u672c\u8eab\u7684\u5411\u91cf\u8868\u793a\u6cd5\uff0c\u4e14\u53ef\u4ee5\u78ba\u5be6\u7684\u5c07\u53e5\u5b50\u7684\u4f4d\u7f6e\u8cc7\u8a0a\u878d\u5165\u6458\u8981\u7684\u9078\u53d6\u4e4b \u4e2d\u3002\u63a5\u8457\uff0c\u6211\u5011\u63a2\u7a76\u5f37\u5316\u5b78\u7fd2\u5c0d\u6458\u8981\u6a21\u578b\u7684\u5f71\u97ff\uff0c\u5be6\u9a57\u7d50\u679c\u5982\u8868 4 \u4e2d\"BERTSUM-Trigram Block+SEP+RL\"\u6240\u793a\u3002\u7d50\u679c\u986f\u793a\uff0c\u52a0\u5165\u5f37\u5316\u5b78\u7fd2\uff0c\u78ba\u5be6\u53ef\u4ee5\u6709\u6548\u5730\u8003\u91cf\u6458\u8981\u7684\u8a55\u4f30\u7d50\u679c \u65bc\u6a21\u578b\u4e4b\u4e2d\uff0c\u56e0\u6b64\u76f8\u8f03\u65bc\"BERTSUM-Trigram Block+SEP\"\uff0c\u53ef\u7372\u5f97\u4e00\u5b9a\u7684\u6210\u6548\u63d0\u5347\u3002\u6700 \u5f8c\uff0c\u7576\u6211\u5011\u65bc\"BERTSUM-Trigram Block+SEP\"\u7684\u8a2d\u5b9a\u4e2d\u518d\u52a0\u5165\u6700\u5927\u908a\u7de3\u76f8\u95dc\u6027\u5411\u91cf\uff0c\u5373 \u6210\u70ba\u672c\u7814\u7a76\u6240\u63d0\u51fa\u4e4b\u57fa\u65bc BERT \u7684\u5f37\u5065\u6027\u62bd\u53d6\u5f0f\u6458\u8981\u6cd5(EBSUM)\uff0c\u7531\u65bc\u878d\u5408\u4e86\u53e5\u5b50\u7684\u4f4d \u7f6e\u8cc7\u8a0a\u3001\u5f37\u5316\u5b78\u7fd2\u4ee5\u53ca\u6700\u5927\u908a\u7de3\u76f8\u95dc\u6027\u6e96\u5247\uff0c\u56e0\u6b64 EBSUM \u53ef\u4ee5\u7372\u5f97\u6700\u4f73\u7684\u6458\u8981\u6210\u6548\u3002 \u7576\u6211\u5011\u9032\u4e00\u6b65\u5730\u5c07 EBSUM \u518d\u8207\u4e09\u9023\u8a5e\u904e\u6ffe\u6cd5\u76f8\u7d50\u5408\u6642(\u5373\"EBSUM+Trigram Block\")\uff0c\u6458 \u5728\u672c\u6587\u4e2d\uff0c\u6211\u5011\u63d0\u51fa\u4e86\u4e00\u5957\u65b0\u7a4e\u7684\u57fa\u65bc BERT \u4e4b\u5f37\u5065\u6027\u62bd\u53d6\u5f0f\u6458\u8981\u6cd5 EBSUM\uff0c\u4e0d\u50c5\u8003 \u91cf\u4e86\u53e5\u5b50\u7684\u4f4d\u7f6e\u8cc7\u8a0a\u3001\u5229\u7528\u5f37\u5316\u5b78\u7fd2\u589e\u5f37\u6458\u8981\u6a21\u578b\u8207\u8a55\u4f30\u6a19\u6e96\u7684\u95dc\u806f\u6027\uff0c\u66f4\u76f4\u63a5\u5730\u5c07\u6700 \u5927\u908a\u7de3\u76f8\u95dc\u6027\u6982\u5ff5\u878d\u5165\u6458\u8981\u6a21\u578b\u4e4b\u4e2d\uff0c\u56e0\u6b64\u5728\u516c\u958b\u7684\u8a55\u6e2c\u8cc7\u6599\u96c6 CNN/DailyMail \u4e2d\uff0c EBSUM \u53ef\u4ee5\u7372\u5f97\u6700\u4f73\u7684\u6458\u8981\u4efb\u52d9\u6210\u6548\u3002\u672a\u4f86\uff0c\u6211\u5011\u5c07\u6301\u7e8c\u6539\u9032 EBSUM \u7684\u6a21\u578b\u67b6\u69cb\uff0c \u4f7f\u5176\u53ef\u4ee5\u66f4\u7c21\u55ae\u3001\u66f4\u6709\u6548\uff0c\u4e5f\u5e0c\u671b\u9a57\u8b49\u6b64\u4e00\u6a21\u578b\u53ef\u4ee5\u5728\u4e0d\u540c\u8a9e\u8a00\u7684\u8cc7\u6599\u96c6\u4e2d\uff0c\u5c55\u73fe\u6458\u8981 \u7684\u6210\u6548\u3002\u6b64\u5916\uff0c\u6211\u5011\u4e5f\u5c07\u628a EBSUM \u61c9\u7528\u65bc\u5176\u4ed6\u4efb\u52d9\u4e4b\u4e2d\uff0c\u8af8\u5982\u8cc7\u8a0a\u6aa2\u7d22\u8207\u6a5f\u5668\u7ffb\u8b6f\u7b49\u3002", "content": "
\u5433\u653f\u80b2\u8207\u9673\u51a0\u5b87
\u8868 2. CNN/DailyMail \u8cc7\u6599\u96c6\u7684\u7d71\u8a08\u6578\u64da\uff0c\u5206\u5225\u4ee5\u55ae\u8a5e\u8207\u53e5\u6578\u8a08\u7b97\u5e73\u5747\u6587\u7ae0\u4ee5\u53ca \u8868 4. \u63a2\u7a76\u672c\u7814\u7a76\u6240\u63d0\u51fa\u7684\u5404\u500b\u6539\u9032\u5143\u4ef6\u4e4b\u5be6\u9a57\u7d50\u679c\u3002
\u6458\u8981\u9577\u5ea6\u3002 [Table 4. Experimental Results of Improved Components in CNN/DailyMail]
[Table 2. Avg words and sentences of Documents and Summary in CNN/DailyMail] ROUGE-1 ROUGE-2 ROUGE-L
BERTSUM-Trigram Block\u6587\u7ae0\u5e73\u5747 42.5619.96\u6458\u8981\u5e73\u5747 39.01
\u7e3d\u6587\u7ae0\u6578 BERTSUM-Trigram Block+CLS\u5b57\u657842.64\u53e5\u6578\u5b57\u6578 20.05\u53e5\u6578 39.11
CNN+DM BERTSUM-Trigram Block+SEP 11490691.942.6828.054.6 20.083.9 39.14
\u5728\u7b2c\u4e8c\u7d44\u5be6\u9a57\u4e2d\uff0c\u6211\u5011\u5c0d\u8fd1\u5e74\u8457\u540d\u7684\u6458\u8981\u7814\u7a76\u9032\u884c\u6bd4\u8f03\u3002\u9996\u5148\uff0c\u6307\u91dd\u751f\u6210\u7db2\u8def(Pointer BERTSUM-Trigram Block+ALL 42.25 19.72 38.67
Generator Network, PGN) (See et al., 2017)\u662f\u76f8\u7576\u77e5\u540d\u7684\u91cd\u5beb\u5f0f\u6458\u8981\u6cd5\uff0c\u4f46\u5176\u7d50\u679c\u751a\u81f3\u8f03 BERTSUM-Trigram Block+SEP+RL 42.71 20.14 39.18
\u62bd\u53d6\u5f0f\u7684\u9818\u5c0e\u6458\u8981\u6cd5(LEAD)\u66f4\u5dee\uff0c\u9019\u662f\u8a31\u591a\u91cd\u5beb\u5f0f\u6458\u8981\u7cfb\u7d71\u7684\u7f3a\u9ede\uff0c\u5373\u5bb9\u6613\u751f\u6210\u8a9e\u610f\u4e0d EBSUM 43.42 20.40 39.78 \u901a\u6216\u4e0d\u6d41\u66a2\u7684\u6458\u8981\uff0c\u5c0e\u81f4\u8a55\u4f30\u7d50\u679c\u4e0d\u4f73\u3002REFRESH (Narayan et al., 2018)\u63d0\u51fa\u4e00\u500b\u4f7f\u7528\u52a0 \u5f37\u5f0f\u5b78\u7fd2\u7684\u62bd\u53d6\u5f0f\u6458\u8981\u6a21\u578b\uff0c\u5176\u4efb\u52d9\u6210\u6548\u8868\u73fe\u512a\u65bc\u6307\u91dd\u751f\u6210\u7db2\u8def(PGN)\u4ee5\u53ca\u9818\u5c0e\u6458\u8981\u6cd5 EBSUM+Trigram Block 43.28 20.16 39.61
(LEAD)\u3002\u7531\u65bc BERT \u7684\u63d0\u51fa\uff0c\u8a31\u591a\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u7684\u76f8\u95dc\u554f\u984c\u7686\u7372\u5f97\u5927\u5e45\u5ea6\u7684\u9032\u6b65\uff0c\u5728\u62bd \u6700\u5f8c\uff0c\u6211\u5011\u9010\u4e00\u7684\u63a2\u7a76\u672c\u7814\u7a76\u6240\u63d0\u51fa\u7684\u5404\u500b\u6539\u9032\u5143\u4ef6\u3002\u9996\u5148\uff0c\u70ba\u4e86\u9a57\u8b49\u53e5\u5b50\u4f4d\u7f6e\u5411
\u53d6\u5f0f\u6458\u8981\u7684\u7814\u7a76\u4e2d\uff0cBERTSUM \u662f\u57fa\u65bc BERT \u800c\u63d0\u51fa\u7684\u7d93\u5178\u65b9\u6cd5\uff0c\u7531\u5be6\u9a57\u7d50\u679c\u53ef\u77e5\uff0c\u4ed6 \u91cf \u5c0d \u6a21 \u578b \u7684 \u5f71 \u97ff \uff0c \u6211 \u5011 \u5728 \u4e0d \u4f7f \u7528 \u4e09 \u9023 \u8a5e \u904e \u6ffe \u6cd5 \u7684 BERTSUM \u4e2d \uff0c \u8868 \u793a
\u8cc7\u6599\u96c6\u9032\u884c\u6458\u8981\u6a21\u578b\u7684\u6548 \u80fd\u8a55\u4f30\uff0c\u542b\u65b0\u805e\u6587\u7ae0\u548c\u5176\u6458\u8981\u6587\u7ae0\uff0c\u5e73\u5747\u5b57\u6578\u4ee5\u53ca\u53e5\u6578\u5982\u8868 2\uff0c\u5176\u4e2d\u8a13\u7df4\u96c6\u3001\u9a57\u8b49\u96c6\u548c \u6e2c\u8a66\u96c6\u5206\u5225\u6709 287,227\u300113,368 \u4ee5\u53ca 11,490 \u7bc7\u6587\u7ae0\u3002\u6211\u5011\u4f7f\u7528 PyTorch\u3001OpenNMT(Klein, Kim, Deng, Senellart, & Rush, 2017)\u4ee5\u53ca bert-base-uncased \u7248\u672c\u7684 BERT \u5be6\u73fe\u672c\u7814\u7a76\u6240\u63d0 \u51fa\u4e4b EBSUM\u3002\u70ba\u4e86\u9032\u884c\u6bd4\u8f03\uff0c\u6211\u5011\u4ea6\u91cd\u73fe\u4e86\u5f37\u5065\u7684\u57fa\u6e96\u7cfb\u7d71 BERTSUM\u3002\u503c\u5f97\u4e00\u63d0\u7684 \u662f\uff0c\u5728 BERTSUM \u7684\u524d\u8655\u7406\u4e2d\uff0c\u6703\u5c07\u5c11\u65bc 5 \u500b\u5b57\u8a5e\u7684\u53e5\u5b50\u9032\u884c\u79fb\u9664\uff0c\u53ef\u80fd\u662f\u56e0\u70ba BERTSUM \u8a8d\u70ba\u5c11\u65bc 5 \u500b\u5b57\u7684\u53e5\u5b50\u8cc7\u8a0a\u91cf\u4e0d\u8db3\uff0c\u4f46\u70ba\u4e86\u8207\u5176\u4ed6\u57fa\u6e96\u7cfb\u7d71\u9032\u884c\u6bd4\u8f03\uff0c\u6211\u5011\u4ea6\u5be6\u4f5c\u5c07\u5c11 \u65bc 5 \u500b\u5b57\u8a5e\u7684\u53e5\u5b50\u9032\u884c\u4fdd\u7559\uff0c\u5176\u9918\u8a2d\u5b9a\u4e0d\u8b8a\u4e4b BERTSUM \u8207\u9818\u5c0e\u6458\u8981\u6cd5(LEAD)\uff0c\u5be6\u9a57\u7d50 \u679c\u5982\u8868 1 \u6240\u793a\u3002\u9019\u7d44\u5be6\u9a57\u5448\u73fe\u4e86\u672c\u7814\u7a76\u6240\u5be6\u73fe\u7684 BERTSUM \u8207\u539f\u8ad6\u6587\u7684\u7d50\u679c\u975e\u5e38\u63a5\u8fd1\uff0c \u4e5f\u56e0\u6b64\u7576\u6211\u5011\u5c07\u50c5\u542b\u6709 5 \u500b\u5b57\u4ee5\u4e0b\u7684\u53e5\u5b50\u4fdd\u7559\u7684\u5be6\u9a57\u7d50\u679c\uff0c\u53ef\u516c\u5e73\u5730\u8207\u5176\u4ed6\u65b9\u6cd5\u4e92\u76f8\u6bd4 \u8f03\u3002 \u8868 1ROUGE-1 ROUGE-2 ROUGE-L \u53ef\u8f03\u9818\u5c0e\u6458\u8981\u3001\u6307\u91dd\u751f\u6210\u7db2\u8def\u4ee5\u53ca REFRESH \u6709\u66f4\u4f73\u7684 ROUGE \u5206\u6578\u3002\u96d6\u7136 BERTSUM \u5df2\u53d6\u5f97\u5927\u5e45\u5ea6\u7684\u9032\u5c55\uff0c\u4f46\u4ed6\u6c92\u6709\u8003\u91cf\u4e86\u53e5\u5b50\u5728\u6587\u7ae0\u4e2d\u7684\u4f4d\u7f6e\u8cc7\u8a0a\u3001\u6458\u8981\u6a21\u578b\u8207\u6458\u8981\u8a55\u4f30 \u65b9\u5f0f\u7684\u95dc\u4fc2\u4e26\u4e0d\u5bc6\u5207\uff0c\u800c\u4e14\u9808\u8981\u501a\u8cf4\u984d\u5916\u7684\u4e09\u9023\u8a5e\u904e\u6ffe\u6cd5\u4ee5\u907f\u514d\u5197\u9918\u8cc7\u8a0a\u7684\u9078\u53d6\u3002\u70ba\u4e86 \u89e3\u6c7a\u9019\u4e9b\u554f\u984c\uff0c\u6211\u5011\u63d0\u51fa\u4e86\u4e00\u5957\u65b0\u7a4e\u7684\u57fa\u65bc BERT \u7684\u5f37\u5065\u6027\u62bd\u53d6\u5f0f\u6458\u8981\u6cd5 EBSUM\uff0c\u5be6 \u9a57\u7d50\u679c\u986f\u793a\uff0cEBSUM \u78ba\u5be6\u76f8\u8f03\u65bc\u9818\u5c0e\u6458\u8981\u3001\u6307\u91dd\u751f\u6210\u7db2\u8def\u3001REFRESH \u4ee5\u53ca BERTSUM \u6709\u66f4\u4f73\u7684 ROUGE \u5206\u6578\u3002 \u8868 3. \u5404\u5f0f\u7d93\u5178\u7684\u6458\u8981\u6a21\u578b\u65bc CNN/DailyMail \u8cc7\u6599\u96c6\u4e0a\u7684\u5be6\u9a57\u7d50\u679c\u3002 [Table 3. Experimental Results of the Classic Models in CNN/DailyMail] ROUGE-1 ROUGE-2 ROUGE-L LEAD 40.32 17.56 36.58 PGN (See, Liu, & Manning, 2017) 39.53 17.28 37.98 REFRESH 41.00 18.80 37.70 \u70ba\"BERTSUM-\u8981\u7684\u6210\u7e3e\u662f\u4e0b\u964d\u7684\uff0c\u9019\u662f\u56e0\u70ba\u6700\u5927\u908a\u7de3\u76f8\u95dc\u6027\u6e96\u5247\u5df2\u81ea\u52d5\u5730\u6703\u5c07\u5197\u9918\u7684\u8cc7\u8a0a\u5c4f\u9664\uff0c\u82e5\u518d (Narayan, Cohen, & Lapata, 2018) \u901a\u904e\u898f\u5247\u5f0f\u7684\u4e09\u9023\u8a5e\u904e\u6ffe\u6cd5\uff0c\u53ef\u80fd\u6703\u904e\u5ea6\u7684\u5c07\u4e00\u4e9b\u503c\u5f97\u9078\u53d6\u5f97\u53e5\u5b50\u6452\u68c4\uff0c\u800c\u9020\u6210\u4efb\u52d9\u6210
BERTSUM (Y. Liu, 2019) \u6548\u964d\u4f4e\u7684\u7d50\u679c\u3002LEAD (Y. Liu, 2019)40.42 43.2517.62 20.2436.67 39.63
\u79fb\u9664 5 \u500b\u5b57\u4ee5 EBSUM 6. \u7d50\u8ad6 (Conclusions) LEAD40.42 43.4217.62 20.4036.66 39.78
\u4e0b\u7684\u53e5\u5b50BERTSUM (Y. Liu, 2019)43.2520.2439.63
BERTSUM43.1520.1639.56
\u4fdd\u7559 5 \u500b\u5b57\u4ee5LEAD40.3217.5636.58
\u4e0b\u7684\u53e5\u5b50BERTSUM43.2520.2039.61
" } } } }