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| "title": "Spoken Document Summarization Using End-to-End Modeling Techniques", | |
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| "abstract": "This thesis set to explore novel and effective end-to-end extractive methods for spoken document summarization. To this end, we propose a neural summarization approach leveraging a hierarchical modeling structure with an attention mechanism to understand a document deeply, and in turn to select representative sentences as", | |
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| "text": "This thesis set to explore novel and effective end-to-end extractive methods for spoken document summarization. To this end, we propose a neural summarization approach leveraging a hierarchical modeling structure with an attention mechanism to understand a document deeply, and in turn to select representative sentences as", | |
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| "text": "Mechanism)(Bahdanau et al., 2015)\u3002\u6ce8\u610f\u529b\u6a5f\u5236\u53ef\u4ee5\u627e\u5230\u6bcf\u500b\u8a9e\u53e5\u8207\u5176\u4ed6\u53e5\u7684 \u95dc\u806f\u6027\uff0c\u56e0\u6b64\u6211\u5011\u53ef\u4ee5\u5c07\u6a21\u578b\u6539\u826f\u6210\u5982\u5716 5 \u7684\u67b6\u69cb\u3002 \u5716 5. \u968e\u5c64\u5f0f\u985e\u795e\u7d93\u6458\u8981\u6a21\u578b -\u7d50\u5408\u6ce8\u610f\u529b\u6a5f\u5236 [Figure 5. Basic architecture with attention mechanism]" | |
| }, | |
| "TABREF1": { | |
| "num": null, | |
| "content": "<table><tr><td>\u57fa\u65bc\u7aef\u5c0d\u7aef\u6a21\u578b\u5316\u6280\u8853\u4e4b\u8a9e\u97f3\u6587\u4ef6\u6458\u8981</td><td>\u5289\u6148\u6069 \u7b49 \u5289\u6148\u6069 \u7b49 37</td></tr><tr><td colspan=\"2\">\u5f80\u662f\u6311\u9078\u8f03\u7b26\u5408\u6458\u8981\u8a9e\u53e5\u7684\u7d50\u679c\uff0c\u56e0\u6b64\u5176\u901a\u5e38\u6c92\u6709\u6839\u64da\u8a9e\u610f\u9032\u884c\u6392\u5e8f\uff0c\u56e0\u6b64\u672c\u8ad6\u6587\u4ea6\u5617 \u2022 \u65b9\u6cd5\uff1a\u6b64\u5206\u985e\u65b9\u5f0f\u6700\u70ba\u5e38\u898b\uff0c\u53ef\u6982\u5206\u70ba\u4e09\u7a2e\uff1a \u4efb\u52d9\u9084\u662f\u6709\u76f8\u7576\u7684\u96e3\u5ea6\uff0c\u56e0\u70ba\u9664\u4e86\u7c21\u55ae\u7684\u5206\u985e\u5916\uff0c\u6211\u5011\u9084\u9700\u7406\u89e3\u4e26\u89e3\u6790\u51fa\u6587\u4ef6\u7684\u91cd\u8981\u8cc7 \u652f\u6301\u4e3b\u65e8\u7684\u76f8\u95dc\u8ad6\u8ff0\u3002\u5982\u4f55\u8b93\u6a21\u578b\u53ef\u4ee5\u6e96\u78ba\u5730\u7406\u89e3\u6587\u4ef6\u4e3b\u984c\u5462\uff1f(Ren et al., 2017)\u91dd\u5c0d\u6b64 \u6b64\u5916\uff0c\u70ba\u4e86\u907f\u514d\u6458\u8981\u7d50\u679c\u53d7\u5230\u904e\u591a\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u7684\u5f71\u97ff\uff0c\u6211\u5011\u5617\u8a66\u52a0\u5165\u8072\u5b78\u7279\u5fb5\u548c</td></tr><tr><td colspan=\"2\">\u8a66\u5c07\u6458\u8981\u8a9e\u53e5\u7684\u6392\u5e8f\u53ca\u6458\u8981\u8a55\u4f30\u6307\u6a19\u61c9\u7528\u65bc\u5f37\u5316\u5b78\u7fd2(Reinforcement learning, RL)\u8f14\u52a9 \uf06e\u7bc0\u9304\u5f0f\u6458\u8981(Summarization by extraction) \u8a0a\uff0c\u624d\u80fd\u77e5\u9053\u54ea\u4e9b\u8a9e\u53e5\u6709\u6a5f\u6703\u6210\u70ba\u6458\u8981\u3002 \u8b70\u984c\u63d0\u51fa\u4e00\u500b\u6709\u6548\u7684\u65b9\u6cd5\uff0c\u5176\u5728\u7522\u751f\u8a9e\u53e5\u5411\u91cf\u8868\u793a\u6642\uff0c\u4ea6\u5c07\u524d\u9762\u7684\u8a9e\u53e5\u4ee5\u53ca\u5f8c\u9762\u7684\u8a9e\u53e5 \u6b21\u8a5e\u5411\u91cf\u8f14\u52a9\u8a13\u7df4\uff1b\u540c\u6642\u6211\u5011\u4ea6\u52a0\u5165\u6ce8\u610f\u529b\u6a5f\u5236\u548c\u5f37\u5316\u5b78\u7fd2\u6a5f\u5236\u65bc\u6a21\u578b\u8a13\u7df4\u4e2d\uff0c\u4ee5\u671f\u80fd</td></tr><tr><td colspan=\"2\">\u3002 \u96d6\u7136\u8a9e\u97f3\u8fa8\u8b58\u7684\u932f\u8aa4\u5c0d\u65bc\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4efb\u52d9\u4e0a\u6703\u6709\u4e00\u5b9a\u7684\u5f71\u97ff\uff0c\u5176\u4e3b\u8981\u7684\u5f71\u97ff\u5728\u65bc \u81ea\u52d5\u8f49\u5beb\u6587\u4ef6\u4e2d\u7684\u5167\u6587\u6703\u8207\u4eba\u5de5\u8f49\u5beb\u7d50\u679c\u6709\u5dee\u7570\uff0c\u9032\u800c\u5c0e\u81f4\u6587\u4ef6\u6458\u8981\u7cfb\u7d71\u7121\u6cd5\u5b8c\u5168\u6e96\u78ba \u5730\u7406\u89e3\u6587\u4ef6\u542b\u7fa9\uff0c\u56e0\u6b64\u4f7f\u5f97\u6458\u8981\u6210\u6548\u4e0d\u4f73\uff1b\u6b64\u5916\uff0c\u6458\u8981\u7684\u5448\u73fe\u4ea6\u662f\u4e00\u9805\u91cd\u8981\u7684\u8ab2\u984c\uff0c\u5982 \u4f55\u5448\u73fe\u51fa\u6613\u65bc\u95b1\u8b80\u7684\u6458\u8981\uff0c\u662f\u6587\u4ef6\u6458\u8981\u7cfb\u7d71\u4e2d\u5fc5\u9808\u5b78\u6703\u7684\u91cd\u9ede\u3002\u800c\u4e00\u500b\u826f\u597d\u7684\u6458\u8981\u8868\u9054 \u61c9\u8a72\u8457\u91cd\u65bc\u4ee5\u4e0b\u56db\u500b\u8981\u7d20\uff1a \u2022 \u8cc7\u8a0a\u6027(Informativity) \uff1a\u6458\u8981\u7d50\u679c\u6240\u5305\u542b\u539f\u6587\u4ef6\u4e2d\u7684\u8cc7\u8a0a\u7a0b\u5ea6\uff0c\u61c9\u76e1\u53ef\u80fd\u6db5\u84cb\u6240\u6709 \u91cd\u8981\u8cc7\u8a0a\u3002 \u2022 \u6587\u6cd5\u6027(Grammaticality) \uff1a\u6458\u8981\u4e2d\u7684\u8a9e\u53e5\u61c9\u7b26\u5408\u8a9e\u8a00\u7684\u6587\u6cd5\uff0c\u6240\u5f97\u4e4b\u6458\u8981\u624d\u6613\u65bc\u95b1 \u8b80\uff1b\u82e5\u4e0d\u7b26\u5408\u6587\u6cd5\uff0c\u5247\u6703\u8f03\u5e38\u88ab\u8996\u70ba\u95dc\u9375\u8a5e\u64f7\u53d6(Keyword Extraction) \u3002\u6b64\u8981\u7d20\u65bc\u91cd \u5beb\u5f0f\u6458\u8981\u4efb\u52d9\u4e0a\u8f03\u53d7\u95dc\u6ce8\u3002 \u2022 \u9023\u8cab\u6027(Coherency) \uff1a\u6b64\u8981\u7d20\u6240\u6307\u7684\u662f\u6458\u8981\u4e2d\u4e0a\u4e0b\u6587\u9593\u7684\u9023\u8cab\u7a0b\u5ea6\uff0c\u82e5\u524d\u5f8c\u53e5\u4e0d\u5b58 \u5728\u9023\u8cab\u6027\uff0c\u5247\u6703\u985e\u4f3c\u65bc\u756b\u91cd\u9ede\u7684\u65b9\u5f0f\u689d\u5217\u51fa\u91cd\u9ede\uff0c\u800c\u975e\u6839\u64da\u6587\u4ef6\u4e3b\u65e8\u6240\u751f\u6210\u4e4b\u6458\u8981\u3002 \u6b64\u8981\u7d20\u65bc\u7bc0\u9304\u5f0f\u6458\u8981\u4efb\u52d9\u4e0a\u5e38\u88ab\u63d0\u53ca\u3002 \u2022 \u975e\u91cd\u8907\u6027(Non-Redundancy) \uff1a\u70ba\u4e86\u80fd\u7c21\u5316\u63cf\u8ff0\uff0c\u61c9\u907f\u514d\u51fa\u73fe\u904e\u591a\u91cd\u8907\u7684\u8a5e\u53e5\u6216\u76f8 \u4f3c\u7684\u8cc7\u8a0a\uff0c\u82e5\u91cd\u8907\u7684\u8cc7\u8a0a\u592a\u591a\u6703\u5f71\u97ff\u4f7f\u7528\u8005\u95b1\u8b80\u3002 \u56e0\u6b64\u672c\u8ad6\u6587\u4e3b\u8981\u6703\u91dd\u5c0d\u4e0a\u8ff0\u4e4b\u8cc7\u8a0a\u6027\u53ca\u9023\u8cab\u6027\u5169\u9805\u8981\u7d20\u8a0e\u8ad6\uff0c\u4e26\u5617\u8a66\u4ee5\u4e0d\u540c\u65b9\u6cd5\u907f \u514d\u53d7\u5230\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u7684\u5f71\u97ff\u3002\u9996\u5148\u65bc\u6458\u8981\u8cc7\u8a0a\u6027\u90e8\u5206\uff0c\u672c\u8ad6\u6587\u767c\u5c55\u4e26\u6539\u9032\u4e00\u500b\u7aef\u5c0d\u7aef\u7684 \u968e\u5c64\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u67b6\u69cb\uff0c\u5176\u53d7\u76ca\u65bc\u647a\u7a4d\u5f0f\u985e\u795e\u7d93\u7db2\u8def(Convolutional neural networks, CNNs)\u4e4b\u8a9e\u8a00\u6a21\u578b\u61c9\u7528\u4ee5\u53ca\u905e\u8ff4\u5f0f\u985e\u795e\u7d93\u7db2\u8def(Recurrent neural networks, RNNs)\u65bc\u81ea \u7136\u8a9e\u8a00\u8655\u7406\u9818\u57df\u7684\u512a\u79c0\u8868\u73fe\uff0c\u4f7f\u5f97\u6211\u5011\u80fd\u5920\u968e\u6bb5\u5f0f(\u5148\u8a9e\u53e5\u5f8c\u5168\u6587)\u5730\u95b1\u8b80\u6587\u4ef6\u4e26\u5feb\u901f \u5730\u7406\u89e3\u8a9e\u610f\uff1b\u53e6\u5916\u6211\u5011\u4ea6\u5617\u8a66\u61c9\u7528\u6ce8\u610f\u529b\u6a5f\u5236(Attention mechanism)\u66f4\u9032\u4e00\u6b65\u63d0\u5347\u6a21 \u578b\u5c0d\u65bc\u6587\u7ae0\u7684\u7406\u89e3\u5ea6\uff0c\u9032\u800c\u63d0\u5347\u6458\u8981\u8cc7\u8a0a\u6027\u3002\u5176\u6b21\u5c0d\u65bc\u6458\u8981\u9023\u8cab\u6027\uff0c\u7531\u65bc\u7bc0\u9304\u5f0f\u6458\u8981\u5f80 \u6a21\u578b\u8a13\u7df4\u3002\u6700\u5f8c\u70ba\u4e86\u907f\u514d\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\uff0c\u6211\u5011\u5728\u6a21\u578b\u9810\u6e2c\u6458\u8981\u7684\u904e\u7a0b\u4e2d\u53c3\u8003\u8a9e\u53e5\u7684\u8072\u5b78 \u7279\u5fb5(Acoustic features)\u53ca\u6b21\u8a5e\u8cc7\u8a0a(Subword information)\uff0c\u5176\u4e2d\u524d\u8005\u5305\u542b\u539f\u8a9e\u97f3\u6587 \u4ef6\u4e2d\u7684\u8a9e\u97f3\u7279\u6027\uff0c\u53ef\u6539\u5584\u5169\u968e\u6bb5\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u7cfb\u7d71\u4e0a\uff0c\u9032\u884c\u6458\u8981\u6642\u7121\u6cd5\u53c3\u8003\u4e4b\u539f\u8a9e\u97f3\u7279 \u6027\uff1b\u800c\u5f8c\u8005\u5247\u662f\u70ba\u4e86\u6539\u5584\u524d\u8ff0\u4e4b\u8a5e\u5f59\u8fa8\u8b58\u932f\u8aa4\uff0c\u56e0\u8fa8\u8b58\u932f\u8aa4\u53ef\u80fd\u767c\u751f\u5728\u8a5e\u5f59\u4e2d\u7684\u90e8\u5206\u5340 \u584a\uff0c\u800c\u5c0e\u81f4\u65b7\u8a5e\u6642\u7121\u6cd5\u8fa8\u5225\u6b63\u78ba\u7684\u8a5e\u5f59\uff0c\u82e5\u4f7f\u7528\u6b21\u8a5e\u8cc7\u8a0a\u5247\u53ef\u4ee5\u4f7f\u7528\u5468\u908a\u8cc7\u8a0a\u63a8\u6e2c\u932f\u8aa4 \u7684\u90e8\u5206\u5176\u6b63\u78ba\u7684\u8a9e\u610f\u3002 2. \u6587\u737b\u56de\u9867 \u81ea\u52d5\u6587\u4ef6\u6458\u8981\u65b9\u6cd5\u4e3b\u8981\u53ef\u4f9d\u7167\u56db\u500b\u9762\u5411\u5206\u985e(\u5982\u5716 1)\uff0c\u53ef\u4f9d\u7167\u4f86\u6e90\u3001\u76ee\u7684\u3001\u529f\u80fd\u53ca\u65b9 \u6cd5\u7b49\u7d30\u5206\u70ba\u4e0d\u540c\u985e\u578b\uff1a \u2022 \u4f86\u6e90\uff1a\u4e3b\u8981\u5206\u70ba\u55ae\u6587\u4ef6\u8207\u591a\u6587\u4ef6\uff0c\u524d\u8005\u6307\u91dd\u5c0d\u55ae\u4e00\u6587\u4ef6\u64f7\u53d6\u6458\u8981\uff0c\u5f8c\u8005\u5247\u662f\u7d71\u6574\u6b78 \u7d0d\u591a\u7bc7\u4e3b\u984c\u76f8\u8fd1\u7684\u6587\u4ef6\u91cd\u9ede\u7522\u751f\u6458\u8981\u3002\u591a\u6587\u4ef6\u6458\u8981\u901a\u5e38\u6703\u8207\u67e5\u8a62\u5171\u540c\u9032\u884c\u70ba\u4ee5\u67e5\u8a62 \u70ba\u4e3b\u4e4b\u591a\u6587\u4ef6\u6458\u8981\uff0c\u540c\u6642\u9032\u884c\u6aa2\u7d22\u8207\u6458\u8981\u3002 \u2022 \u76ee\u7684\uff1a\u53ef\u5206\u70ba\u4e00\u822c\u6027\u548c\u67e5\u8a62\u5c0e\u5411\uff0c\u4e00\u822c\u6027\u7684\u6458\u8981\u4e3b\u8981\u5c08\u6ce8\u5728\u6587\u4ef6\u4e2d\u7684\u4e3b\u8981\u91cd\u9ede\uff1b\u800c \u67e5\u8a62\u5c0e\u5411\u5247\u6703\u6839\u64da\u67e5\u8a62\u5b57\u4e32\u6c7a\u5b9a\u5176\u6458\u8981\u5167\u5bb9\uff0c\u800c\u67e5\u8a62\u5c0e\u5411\u7684\u6458\u8981\u901a\u5e38\u6703\u8207\u591a\u6587\u4ef6\u6458 \u8981\u540c\u6642\u51fa\u73fe\u3002 \u8cc7\u8a0a\uff1b\u800c\u8f03\u5c11\u6578\u70ba\u6307\u793a\u6027\u548c\u6279\u5224\u6027\uff0c\u6b64\u4e8c\u8005\u7d66\u4e88\u7684\u6458\u8981\u7686\u4e0d\u5305\u542b\u539f\u6587\u7684\u91cd\u8981\u5167\u5bb9\uff0c \u524d\u8005\u6703\u6307\u51fa\u6587\u4ef6\u7684\u984c\u76ee\u6216\u9818\u57df\u7b49\u8a6e\u91cb\u8cc7\u6599(Metadata) \uff1b\u800c\u5f8c\u8005\u5247\u662f\u6703\u5224\u65b7\u6574\u4efd\u6587\u4ef6 \u662f\u6b63\u9762\u7684\u9084\u662f\u8ca0\u9762\u7684\u3002 \u8a9e\u53e5\u300c\u662f\u5426\u300d\u70ba\u6458\u8981\u3002\u800c\u5206\u985e\u554f\u984c\u5728\u6df1\u5c64\u5b78\u7fd2\u6280\u8853\u4e2d\u662f\u6700\u57fa\u672c\u7684\u554f\u984c\uff0c\u4f46\u662f\u7bc0\u9304\u5f0f\u6458\u8981 \u7136\u800c\uff0c\u5c0d\u65bc\u7bc0\u9304\u5f0f\u6458\u8981\u4efb\u52d9\u4f86\u8aaa\uff0c\u6a21\u578b\u5c0d\u6587\u4ef6\u7684\u7406\u89e3\u61c9\u8a72\u8981\u80fd\u9054\u5230\u652f\u6490\u5f8c\u7e8c\u5206\u985e\u6458 \u5f0f\uff0c\u6587\u4ef6\u4e3b\u65e8\u53ef\u80fd\u5206\u6563\u65bc\u6587\u4ef6\u7684\u4e0d\u540c\u90e8\u5206\uff0c\u9664\u53bb\u6587\u4ef6\u4e3b\u65e8\u7684\u6bb5\u843d\uff0c\u6587\u4ef6\u7684\u5176\u4ed6\u90e8\u5206\u61c9\u70ba \u8fa8\u5225\u53ca\u6392\u5e8f\u6458\u8981\u53e5\u3002 \u8981\u8a9e\u53e5\u7684\u7a0b\u5ea6\uff0c\u610f\u5373\u6a21\u578b\u6240\u5f97\u4e4b\u6587\u4ef6\u5411\u91cf\u8868\u793a\u61c9\u5b8c\u6574\u6db5\u84cb\u6587\u4ef6\u4e3b\u65e8\u3002\u6839\u64da\u4e0d\u540c\u7684\u64b0\u5beb\u65b9 \u5c0d\u61c9\u7684\u8a9e\u53e5\u8868\u793a\uff0c\u518d\u5f9e\u8a9e\u53e5\u8868\u793a\u4e2d\u5b78\u7fd2\u5230\u6587\u4ef6\u4e2d\u7684\u91cd\u8981\u6982\u5ff5\uff0c\u4ea6\u53ef\u7a31\u70ba\u6587\u4ef6\u8868\u793a\uff1b\u6700\u5f8c \u6703\u5c07\u8a9e\u53e5\u8868\u793a\u53ca\u6587\u4ef6\u8868\u793a\u7686\u653e\u7f6e\u65bc\u8a9e\u53e5\u9078\u53d6\u5668\u4e2d\uff0c\u4f7f\u5176\u80fd\u5920\u6839\u64da\u6587\u4ef6\u8868\u793a\u53ca\u8a9e\u53e5\u8868\u793a\uff0c 2.1 \u5728\u7bc0\u9304\u5f0f\u6587\u4ef6\u6458\u8981\u4efb\u52d9\u4e2d\uff0c\u6211\u5011\u901a\u5e38\u53ef\u4ee5\u5c07\u5176\u8996\u70ba\u5206\u985e\u554f\u984c\uff0c\u56e0\u70ba\u6211\u5011\u8981\u5224\u65b7\u6587\u4ef6\u4e2d\u7684 \u4e00\u9805\u8df3\u8e8d\u6027\u5730\u6210\u9577\u3002 \u7a31\u4e4b\u70ba\u8a9e\u53e5\u9078\u53d6\u5668\u3002\u968e\u5c64\u5f0f\u7de8\u78bc\u5668\u4e2d\u4e3b\u8981\u6709\u5169\u500b\u968e\u5c64\uff0c\u6211\u5011\u6703\u5148\u91dd\u5c0d\u6587\u4ef6\u4e2d\u7684\u8a9e\u53e5\u627e\u5230 \u2022 \u529f\u80fd\uff1a\u5927\u591a\u6578\u6458\u8981\u662f\u8cc7\u8a0a\u6027\u7684\uff0c\u4e3b\u8981\u5c08\u6ce8\u5728\u7522\u751f\u539f\u6587\u4ef6\u7684\u7c21\u77ed\u7248\u672c\uff0c\u80fd\u4fdd\u7559\u5176\u91cd\u8981 (Cheng & Lapata, 2016) \u5c07\u7bc0\u9304\u5f0f\u6458\u8981\u4efb\u52d9\u8996\u70ba\u4e00\u7a2e\u5e8f\u5217\u6a19\u8a18\u53ca\u6392\u5e8f\u554f\u984c\uff0c\u5176\u65b9\u6cd5\u4e3b \u8207\u8a72\u53e5\u7684\u76f8\u95dc\u6027\u4e32\u63a5\uff0c\u540c\u6642\u653e\u5165\u4e00\u4e9b\u8207\u8a72\u53e5\u76f8\u95dc\u7684\u4eba\u5de5\u7279\u5fb5(\u8a9e\u53e5\u9577\u5ea6\u3001\u4f4d\u7f6e\u7b49)\uff0c\u4f7f \u589e\u52a0\u6458\u8981\u7684\u8cc7\u8a0a\u6027\u3002 \uf06e\u91cd\u5beb\u5f0f\u6458\u8981(Summarization by abstraction) \u8981\u7684\u7279\u8272\u5728\u65bc\u4f7f\u7528\u4e00\u968e\u5c64\u5f0f\u7de8\u78bc\u5668\u548c\u542b\u6709\u6ce8\u610f\u529b\u6a5f\u5236(Attention Mechanism)\u7684\u89e3\u78bc\u5668\u3002\u968e \u5f97\u5206\u985e\u6642\u80fd\u4f7f\u7528\u66f4\u5177\u8a9e\u610f\u7684\u8a9e\u53e5\u5411\u91cf\u3002\u6b64\u65b9\u6cd5\u4e4b\u67b6\u69cb\u76f8\u7576\u5927\uff0c\u4f46\u5f97\u5230\u4e4b\u6458\u8981\u6548\u679c\u4e5f\u76f8\u7576 \uf06e\u8a9e\u53e5\u58d3\u7e2e\u5f0f\u6458\u8981(Summarization by sentence compression) \u7bc0\u9304\u5f0f\u6458\u8981\u8207\u91cd\u5beb\u5f0f\u6458\u8981\u7684\u512a\u7f3a\uff0c\u4ee5\u5b78\u7fd2\u8005\u70ba\u4f8b\uff0c\u4e00\u500b\u597d\u7684\u5b78\u7fd2\u8005\u5728\u64b0\u5beb\u6458\u8981\u6642\u6703 \u5148\u95b1\u8b80\u904e\u6574\u7bc7\u6587\u7ae0\uff0c\u518d\u4ee5\u81ea\u5df1\u7684\u65b9\u5f0f\u64b0\u5beb\uff0c\u800c\u5f97\u4e4b\u6458\u8981\u5167\u5bb9\u80fd\u524d\u5f8c\u901a\u9806\u4e14\u7b26\u5408\u6587\u7ae0 \u65e8\u610f\uff1b\u800c\u4e0d\u597d\u7684\u5b78\u7fd2\u8005\u5728\u64b0\u5beb\u6458\u8981\u6642\uff0c\u53ea\u6703\u5927\u7565\u770b\u904e\u6587\u7ae0\uff0c\u4e26\u4e14\u6311\u9078\u51fa\u300c\u53ef\u80fd\u300d\u91cd \u8981\u7684\u8a9e\u53e5\uff0c\u7d44\u5408\u5728\u4e00\u8d77\u4f5c\u70ba\u6458\u8981\u3002\u4f46\u6b64\u65b9\u6cd5\u5f97\u5230\u4e4b\u6458\u8981\u53ef\u80fd\u5305\u542b\u67d0\u4e9b\u4e0d\u76f8\u95dc\u7684\u5167\u5bb9\uff0c \u4e14\u8a9e\u53e5\u9593\u7684\u929c\u63a5\u53ef\u80fd\u6703\u6709\u5167\u5bb9\u4e0d\u9023\u8cab\u6216\u4e0d\u901a\u9806\u7684\u60c5\u6cc1\u767c\u751f\u3002\u9664\u4e86\u8f03\u5e38\u898b\u7684\u7bc0\u9304\u5f0f\u6458 \u8981\u53ca\u91cd\u5beb\u5f0f\u6458\u8981\u5916\uff0c\u8a9e\u53e5\u58d3\u7e2e\u5f0f\u6458\u8981\u6bd4\u8f03\u7279\u5225\u4e00\u9ede\uff0c\u4e3b\u8981\u7528\u65bc\u5c07\u8a9e\u53e5\u9577\u5ea6\u7e2e\u6e1b\uff0c\u6b64 \u65b9\u6cd5\u53ef\u8207\u7bc0\u9304\u5f0f\u6458\u8981\u5171\u540c\u4f7f\u7528\uff0c\u800c\u76ee\u524d\u901a\u5e38\u6703\u5c07\u6b64\u65b9\u6cd5\u6b78\u985e\u70ba\u91cd\u5beb\u5f0f\u6458\u8981\u7684\u4e00\u90e8 \u5206\u3002 \u672c\u8ad6\u6587\u4e3b\u8981\u5c08\u6ce8\u65bc\u4e00\u822c\u6027\u55ae\u6587\u4ef6\u7bc0\u9304\u5f0f\u6458\u8981\u7684\u7814\u7a76\u3002\u6b64\u5916\u6458\u8981\u4ea6\u53ef\u91dd\u5c0d\u6587\u4ef6\u5f62\u5f0f\u5206 \u985e\uff0c\u5982\u5e38\u898b\u7684\u6587\u5b57\u6587\u4ef6 (Text documents) \u53ca\u5305\u542b\u8a9e\u97f3\u8cc7\u8a0a\u7684\u8a9e\u97f3\u6587\u4ef6 (Spoken documents) \uff0c \u91dd\u5c0d\u4e0d\u540c\u6587\u4ef6\u5f62\u5f0f\uff0c\u6240\u4f7f\u7528\u7684\u6458\u8981\u6a21\u578b\u7d30\u7bc0\u4e5f\u61c9\u6709\u6240\u8b8a\u5316\u3002\u6587\u5b57\u6587\u4ef6\u6458\u8981\u4fc2\u6307\u4e00\u822c\u4ee5\u6587 \u5b57\u5167\u5bb9\u70ba\u4e3b\u7684\u6587\u4ef6\u7522\u751f\u4e4b\u6458\u8981\uff0c\u5927\u90e8\u5206\u7684\u6458\u8981\u7814\u7a76\u90fd\u5c6c\u65bc\u6587\u5b57\u6587\u4ef6\u6458\u8981\uff1b\u800c\u8a9e\u97f3\u6587\u4ef6\u6458 \u8981\u5247\u662f\u4f7f\u7528\u542b\u6709\u8a9e\u97f3\u8cc7\u8a0a\u7684\u6587\u4ef6\uff0c\u901a\u5e38\u662f\u900f\u904e\u8a9e\u97f3\u8fa8\u8b58\u5f8c\u5f97\u5230\u7684\u8f49\u5beb\u6587\u4ef6\uff0c\u5176\u4e2d\u53ef\u80fd\u6703 \u542b\u6709\u4e00\u4e9b\u8a9e\u97f3\u8fa8\u8b58\u7522\u751f\u4e4b\u932f\u8aa4\uff0c\u4ee5\u53ca\u53e3\u8a9e\u4e0a\u7121\u610f\u7fa9\u7684\u8cc7\u8a0a\u3002\u56e0\u6b64\uff0c\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u6703\u6bd4\u6587 \u5b57\u6587\u4ef6\u6458\u8981\u66f4\u70ba\u56f0\u96e3\uff0c\u53cd\u4e4b\uff0c\u8a9e\u97f3\u6587\u4ef6\u5305\u542b\u8a9e\u97f3\u8cc7\u8a0a\uff0c\u53ef\u4ee5\u63d0\u4f9b\u6458\u8981\u65b9\u6cd5\u66f4\u591a\u6709\u610f\u7fa9\u7684 \u8cc7\u8a0a\uff0c\u80fd\u6709\u6548\u5730\u62b5\u92b7\u5176\u8fa8\u8b58\u932f\u8aa4\u3002 \u6b64\u5916\uff0c\u6709\u9452\u65bc\u6df1\u5c64\u5b78\u7fd2\u7684\u84ec\u52c3\u767c\u5c55\uff0c\u73fe\u4eca\u7684\u6280\u8853\u5927\u591a\u662f\u4ee5\u7aef\u5c0d\u7aef\u7684\u6df1\u5c64\u985e\u795e\u7d93\u7db2\u8def \u67b6\u69cb\u70ba\u4e3b\u3002\u6df1\u5c64\u5b78\u7fd2\u4e3b\u8981\u662f\u6a21\u64ec\u4eba\u985e\u4e4b\u5b78\u7fd2\u6a21\u5f0f\uff0c\u5c07\u6df1\u5c64\u985e\u795e\u7d93\u7db2\u8def\u67b6\u69cb\u8996\u70ba\u4eba\u985e\u5927\u8166 \u795e\u7d93\u7cfb\u7d71\uff0c\u4e26\u8f14\u4ee5\u5927\u91cf\u8cc7\u6599\u9032\u884c\u8a13\u7df4\uff0c\u4f7f\u5176\u80fd\u5920\u5b78\u7fd2\u5982\u4f55\u89e3\u6c7a\u8a72\u7814\u7a76\u554f\u984c\u3002\u5176\u67b6\u69cb\u4e2d\u4e3b \u8981\u5b78\u7fd2\u7684\u662f\u8f38\u5165\u8207\u8f38\u51fa\u4e4b\u9593\u7684\u95dc\u4fc2\uff0c\u85c9\u7531\u5c07\u4e0d\u540c\u7684\u8f38\u5165\u6a23\u672c\u6295\u5f71\u81f3\u76f8\u540c\u7684\u7a7a\u9593\u4e2d\uff0c\u6211\u5011 \u6e2c\u6458\u8981\u8207\u6a19\u6e96\u6458\u8981\u7684\u8a55\u4f30\u5206\u6578\uff0c\u800c\u6b64\u65b9\u6cd5\u8b93\u6a21\u578b\u6536\u6582\u901f\u5ea6\u589e\u52a0\uff0c\u540c\u6642\u4e5f\u63d0\u5347\u6e96\u78ba\u5ea6\uff0c\u662f \u4ef6\u4e3b\u984c\u53ca\u6982\u5ff5\u3002\u56e0\u6b64\uff0c\u6211\u5011\u63d0\u51fa\u4e00\u57fa\u672c\u67b6\u69cb\uff0c\u5176\u4e2d\u5305\u542b\u4e00\u968e\u5c64\u5f0f\u7de8\u78bc\u5668\u53ca\u4e00\u89e3\u78bc\u5668\uff0c\u4ea6 \u6587\u737b\u63a2\u8a0e\u5c07\u4ee5\u7aef\u5c0d\u7aef\u4e4b\u6df1\u5c64\u5b78\u7fd2\u65b9\u6cd5\u70ba\u4e3b\u3002 \u4f7f\u5176\u80fd\u5920\u5f80\u6211\u5011\u671f\u5f85\u7684\u65b9\u5411\u9032\u884c\u8a13\u7df4\u3002(Narayan et al., 2018a)\u6240\u4f7f\u7528\u7684\u734e\u52f5\u5206\u6578\u662f\u4f7f\u7528\u9810 Gradient)\u554f\u984c\uff0c\u540c\u6642\u900f\u904e\u4e0d\u65b7\u66f4\u65b0\u8a18\u61b6\u55ae\u5143\uff0c\u80fd\u4fdd\u7559\u66f4\u591a\u91cd\u8981\u8cc7\u8a0a\uff0c\u4e0d\u6703\u96a8\u8457\u6642\u9593\u592a\u9577 \u8981\u7684\u8a9e\u53e5\uff0c\u4e14\u540c\u6642\u80fd\u5b78\u7fd2\u5230\u6458\u8981\u8a9e\u53e5\u9593\u6709\u610f\u7fa9\u7684\u6392\u5e8f\uff0c\u4f7f\u5f97\u6458\u8981\u5167\u5bb9\u80fd\u66f4\u6d41\u66a2\u5730\u8868\u9054\u6587 \u5373\u53ef\u5728\u8a72\u7a7a\u9593\u4e2d\u5c07\u6bcf\u500b\u8f38\u5165\u6a23\u672c\u5c0d\u61c9\u81f3\u6b63\u78ba\u7684\u8f38\u51fa\uff0c\u9032\u800c\u5f97\u5230\u6b63\u78ba\u7684\u7d50\u679c\u3002\u56e0\u6b64\u5f8c\u7e8c\u4e4b \u6b64\u5916\uff0c(Cheng & Lapata, 2016)\u9084\u5617\u8a66\u7528\u7bc0\u9304\u5f0f\u7684\u65b9\u6cd5\u6a21\u64ec\u51fa\u91cd\u5beb\u5f0f\u6458\u8981\uff0c\u8207\u524d\u8ff0\u6a19\u8a18\u8a9e \u53e5\u7684\u4e0d\u540c\uff0c\u4e3b\u8981\u662f\u5f9e\u539f\u6587\u4ef6\u4e2d\u6311\u9078\u55ae\u8a5e\u5f8c\u7d44\u5408\u6210\u6458\u8981\u53e5\uff0c\u800c\u751f\u6210\u4e4b\u6458\u8981\u76f8\u7576\u4e0d\u7b26\u5408\u6587\u6cd5 \u6027\u4e5f\u4e0d\u901a\u9806\uff0c\u4e0d\u904e\u95dc\u9375\u8a5e\u5f59\u57fa\u672c\u4e0a\u90fd\u80fd\u6db5\u84cb\u3002\u4ee5\u6b64\u5f97\u77e5\uff0c(Cheng & Lapata)\u7684\u65b9\u6cd5\u5728\u8a9e\u8a00 \u7406\u89e3(Language Understanding)\u53ca\u8cc7\u8a0a\u64f7\u53d6(Information Extraction)\u6709\u4e0d\u932f\u7684\u6210\u6548\u3002 \u9664\u4e86(Cheng & Lapata, 2016)\u540c\u6642\u9032\u884c\u7bc0\u9304\u5f0f\u6458\u8981\u8207\u91cd\u5beb\u5f0f\u6458\u8981\u7684\u7814\u7a76\u5916\uff0c(Nallapati et al., 2017)\u63d0\u51fa\u7684 SummaRuNNer \u4ea6\u5617\u8a66\u751f\u6210\u91cd\u5beb\u5f0f\u6458\u8981\u3002\u8207(Cheng & Lapata, 2016)\u4e0d \u540c\u4e4b\u8655\u5728\u65bc SummaRuNNer \u5728\u7bc0\u9304\u5f0f\u6458\u8981\u4efb\u52d9\u4e0a\uff0c\u4e26\u975e\u4f7f\u7528\u7de8\u78bc-\u89e3\u78bc\u5668\u67b6\u69cb\uff0c\u50c5\u662f\u55ae\u7d14 \u5730\u5efa\u7acb\u5169\u5c64\u96d9\u5411 RNN \u5f8c\u4fbf\u5224\u65b7\u8a9e\u53e5\u6a19\u8a18\u70ba\u4f55\u3002\u76f8\u4f3c\u4e4b\u8655\u5728\u65bc\u5176 RNN \u4e5f\u662f\u968e\u5c64\u5f0f\u7684\u67b6\u69cb\uff0c \u7b2c\u4e00\u5c64\u8f38\u5165\u70ba\u8a5e\u5f59\u5411\u91cf\uff0c\u7b2c\u4e8c\u5c64\u5247\u662f\u7b2c\u4e00\u5c64\u8f38\u51fa\u6240\u5f97\u4e4b\u8a9e\u53e5\u5411\u91cf\u3002\u6b64\u7a2e\u4f5c\u6cd5\u4e2d\u4f7f\u7528\u7684\u53c3 \u6578\u91cf\u8f03\u5c11\uff0c\u56e0\u6b64\u6536\u6582\u901f\u5ea6\u4e5f\u8f03\u70ba\u5feb\u901f\u3002\u9664\u4e86\u7bc0\u9304\u5f0f\u6458\u8981\u4efb\u52d9\u5916\uff0c(Nallapati et al., 2017)\u4e5f \u5617\u8a66\u5c07\u6700\u5f8c\u4e00\u5c64\u9810\u6e2c\u6a19\u8a18\uff0c\u6539\u70ba\u4e00\u500b\u7c21\u6613\u89e3\u78bc\u5668\u7528\u65bc\u91cd\u5beb\u5f0f\u6458\u8981\u4efb\u52d9\u3002\u6b64\u5916\uff0c\u7531\u65bc\u6458\u8981 \u4efb\u52d9\u4f7f\u7528\u4e4b\u8cc7\u6599\u96c6\u4e00\u822c\u662f\u6c92\u6709\u6458\u8981\u6a19\u8a18\u7684\uff0c(Nallapati et al., 2017)\u63d0\u51fa\u4e00\u7a2e\u8caa\u5a6a\u6cd5\u5c0d\u6bcf\u500b \u8a9e\u53e5\u6a19\u8a18\u6458\u8981\uff0c\u9019\u500b\u65b9\u6cd5\u80fd\u5920\u627e\u5230\u8f03\u597d\u7684\u6458\u8981\u7d44\u5408\u800c\u975e\u53ea\u662f\u627e\u55ae\u7368\u6bd4\u5c0d\u6bcf\u53e5\u7684\u91cd\u8981\u6027\uff0c \u4ea6\u6709\u8a31\u591a\u5b78\u8005\u5617\u8a66\u5c07\u6b64\u65b9\u6cd5\u7528\u65bc\u81ea\u8eab\u7684\u4efb\u52d9\u4e0a\u3002 \u96a8\u8457\u8fd1\u5e7e\u5e74\u5f37\u5316\u5b78\u7fd2(Reinforcement Learning)\u7684\u71b1\u6f6e\uff0c\u4ea6\u6709\u5b78\u8005\u5c07\u5f37\u5316\u5b78\u7fd2\u61c9\u7528\u65bc \u7bc0\u9304\u5f0f\u6458\u8981\u4efb\u52d9\u4e0a\uff0c(Narayan, Cohen & Lapata, 2018a)\u70ba\u4e86\u89e3\u6c7a\u524d\u8ff0\u4e4b\u7bc0\u9304\u5f0f\u6458\u8981\u6c92\u6709\u6b63 \u78ba\u6458\u8981\u6a19\u8a18\u7684\u60c5\u6cc1\uff0c\u56e0\u6b64\u52a0\u5165\u5f37\u5316\u5b78\u7fd2\u3002\u5176\u4e3b\u8981\u67b6\u69cb\u662f\u6539\u826f\u81ea(Cheng & Lapata, 2016)\uff0c \u4e0d\u540c\u4e4b\u8655\u5728\u65bc\u5176\u5728\u7b2c\u4e8c\u5c64\u7de8\u78bc\u5668\u7684\u8a9e\u53e5\u8f38\u5165\u662f\u4ee5\u5012\u5e8f\u65b9\u5f0f\u8f38\u5165\uff0c\u56e0\u70ba\u5927\u591a\u6578\u6587\u4ef6\u901a\u5e38\u6703 \u5c07\u4e3b\u65e8\u7f6e\u65bc\u8f03\u524d\u9762\u7684\u6bb5\u843d\uff0c\u518d\u52a0\u4e0a RNN \u6bd4\u8f03\u5bb9\u6613\u8a18\u5f97\u5f8c\u9762\u6642\u9593\u9ede\u8cc7\u8a0a\u7684\u7279\u6027\uff0c\u6b64\u65b9\u5f0f \u80fd\u5920\u5c07\u91cd\u8981\u8cc7\u8a0a\u66f4\u6e05\u695a\u8a18\u5f97\u3002(Narayan et al., 2018a)\u6240\u4f7f\u7528\u7684\u5f37\u5316\u5b78\u7fd2\u65b9\u6cd5\uff0c\u662f\u6700\u57fa\u790e\u7684 \u7b56\u7565\u68af\u5ea6(Policy Gradient) \uff0c\u4e5f\u5c31\u662f\u900f\u904e\u8a08\u7b97\u5f97\u4e4b\u734e\u52f5(Reward)\u5206\u6578\u8207\u6a21\u578b\u8a13\u7df4\u68af\u5ea6\u52a0\u6210\uff0c \u7de8\u78bc\u5668\u8f38\u5165\u90fd\u662f\u6574\u7bc7\u6587\u7ae0\u7684\u6bcf\u500b\u8a5e\u5f59\uff0c\u4e0d\u8003\u616e\u8a9e\u53e5\u7684\u5206\u754c\uff0c\u800c\u968e\u5c64\u5f0f\u7de8\u78bc\u5668\u7b2c\u4e00\u5c64\u7684\u8f38 \u5165\u4e00\u6a23\u662f\u6574\u7bc7\u6587\u7ae0\u7684\u6bcf\u500b\u8a5e\u5f59\uff0c\u7576\u9047\u5230\u6bcf\u500b\u8a9e\u53e5\u7684\u7d50\u5c3e\u8a5e\u6642\uff0c\u5c31\u6703\u5c07\u6b64\u6642\u7684\u8f38\u51fa\u5411\u91cf\u8996 \u70ba\u8a9e\u53e5\u7684\u5411\u91cf\u8868\u793a\uff0c\u4e26\u4f5c\u70ba\u7b2c\u4e8c\u5c64\u7684\u8f38\u5165\uff0c\u4e5f\u5c31\u662f\u8aaa\uff0c\u7b2c\u4e8c\u5c64\u7684\u8f38\u5165\u662f\u6587\u7ae0\u4e2d\u7684\u8a9e\u53e5\uff0c \u9019\u7a2e\u65b9\u6cd5\u80fd\u5920\u5f97\u5230\u66f4\u7d30\u90e8\u7684\u6587\u4ef6\u8cc7\u8a0a\uff0c\u4e5f\u4f7f\u5f97\u7522\u751f\u4e4b\u6458\u8981\u5167\u5bb9\u8f03\u7b26\u5408\u6587\u7ae0\u4e3b\u65e8\u3002\u96d6\u7136\u5728 (Nallapati et al., 2016) \u5df2\u7d93\u6709\u5617\u8a66\u5c07 Pointer Network \u7684\u60f3\u6cd5\u7d50\u5408\u9032\u6a21\u578b\u4e2d\uff0c\u4f46\u662f\u6b64\u7a2e\u65b9 \u6cd5\u904e\u65bc\u5f37\u786c\uff0c\u56e0\u70ba\u6b64\u63a7\u5236\u5668\u5f97\u5230\u7684\u7d50\u679c\u50c5\u80fd\u4e8c\u9078\u4e00\u3002 \u56e0\u6b64 (See et al., 2017) \u63d0\u51fa\u7684\u67b6\u69cb\u80fd\u6709\u6548\u7684\u89e3\u6c7a\u6b64\u72c0\u6cc1\uff0c\u6b64\u7bc7\u7814\u7a76\u63d0\u51fa\u7684\u65b9\u6cd5\u662f\u4ee5 \u540c\u6642\u9032\u884c\u7522\u751f\u65b0\u8a5e\u8207\u9078\u53d6\u539f\u6709\u8a5e\u5f59\u7684\u52d5\u4f5c\uff0c\u6700\u5f8c\u5229\u7528\u4e00\u6a5f\u7387\u503c\u7c21\u55ae\u7dda\u6027\u7d50\u5408\u5169\u8005\u6240\u5f97\u5230 \u7684\u6a5f\u7387\u5206\u4f48\uff0c\u4ee5\u6b64\u5f97\u5230\u6700\u7d42\u7684\u8a5e\u5178\u6a5f\u7387\u5206\u4f48\uff0c\u8a5e\u5178\u4e2d\u5305\u542b\u89e3\u78bc\u8a5e\u5178\u8207\u8f38\u5165\u6587\u4ef6\u7684\u8a5e\u5f59\u3002 \u6b64\u5916\uff0c(See et al., 2017)\u4ea6\u63d0\u51fa\u4e00\u7a2e Coverage \u6a5f\u5236\uff0c\u6b64\u6a5f\u5236\u4e3b\u8981\u662f\u70ba\u4e86\u89e3\u6c7a\u5728\u8a9e\u8a00\u751f\u6210\u4efb \u52d9\u4e0a\u5bb9\u6613\u51fa\u73fe OOV \u548c\u91cd\u8907\u8a5e\u7684\u554f\u984c\uff0c\u5176\u5728\u6bcf\u500b\u6642\u9593\u9ede\u6703\u5c07\u4ee5\u524d\u6642\u9593\u9ede\u5f97\u5230\u7684\u6ce8\u610f\u529b\u5206 \u4f48\u52a0\u7e3d\u5f8c\u4f5c\u70ba\u4e00 coverage \u5411\u91cf\uff0c\u7dad\u5ea6\u5927\u5c0f\u70ba\u7de8\u78bc\u5668\u7684\u6642\u9593\u9ede\u6578\u91cf\uff0c\u800c\u5f8c\u5728\u7576\u524d\u6642\u9593\u9ede\u6703 \u53c3\u8003\u6b64\u5411\u91cf\u8a08\u7b97\u6ce8\u610f\u529b\u5206\u4f48\uff0c\u540c\u6642\u4e5f\u6703\u5c07\u6b64\u5411\u91cf\u548c\u6ce8\u610f\u529b\u5206\u4f48\u9032\u884c\u6bd4\u8f03\uff0c\u627e\u51fa\u6bcf\u500b\u7dad\u5ea6 \u6700\u5c0f\u503c\u5f8c\u52a0\u7e3d\u4fbf\u5f97\u5230\u4e00 coverage \u640d\u5931\uff0c\u4e4b\u5f8c\u6703\u505a\u70ba\u8a13\u7df4\u6642\u4f7f\u7528\u7684\u61f2\u7f70\u503c\uff0c\u8b93\u6a21\u578b\u53ef\u4ee5\u5c07 \u91cd\u8907\u8a5e\u7684\u6a5f\u7387\u964d\u4f4e\u3002\u6b64\u7814\u7a76\u6240\u5f97\u5230\u7684\u6458\u8981\u6548\u679c\u6bd4\u4ee5\u5f80\u7684\u91cd\u5beb\u5f0f\u6458\u8981\u512a\u7570\u8a31\u591a\uff0c\u800c\u5be6\u9a57\u7d50 \u679c\u4ea6\u986f\u793a\u6458\u8981\u6210\u679c\u6bd4\u8f03\u504f\u5411\u65bc\u7bc0\u9304\u5f0f\u6458\u8981\uff0c\u56e0\u70ba\u8907\u88fd\u7684\u6bd4\u4f8b\u6bd4\u751f\u6210\u7684\u6bd4\u4f8b\u9ad8\u51fa\u8a31\u591a\uff0c\u8207 \u6b64\u540c\u6642\u6211\u5011\u4e5f\u767c\u73fe\u7bc0\u9304\u5f0f\u6458\u8981\u7684\u6210\u6548\u4ecd\u6bd4\u91cd\u5beb\u5f0f\u6458\u8981\u66f4\u70ba\u986f\u8457\u3002 3. \u968e\u5c64\u5f0f\u985e\u795e\u7d93\u6458\u8981\u6a21\u578b \u6211\u5011\u5c07\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u554f\u984c\u8996\u70ba\u4e00\u8a9e\u53e5\u5206\u985e\u66a8\u6392\u5e8f\u554f\u984c\uff0c\u4ee5\u671f\u80fd\u4f9d\u6587\u4ef6\u4e3b\u65e8\u9078\u51fa\u53ef\u80fd\u70ba\u6458 \u800c((output gate)\uff0c\u4ee5\u53ca\u4e00\u500b\u8a18\u61b6\u55ae\u5143 (memory cell)\uff0c\u6240\u4ee5\u53ef\u4ee5\u6539\u5584\u6d88\u5931\u7684\u68af\u5ea6(Vanishing \u8a5e\u5f59\uff0c\u5c31\u9700\u8981\u5f9e\u8f38\u5165\u8cc7\u6599\u4e2d\u8907\u88fd\u4f7f\u7528\uff1b\u6700\u5f8c\u5247\u662f\u5c07\u7de8\u78bc\u5668\u6539\u6210\u968e\u5c64\u5f0f\u7684\u7de8\u78bc\u5668\uff0c\u4e00\u822c\u7684 \u4f46\u4e0d\u5f71\u97ff\u5176\u8a9e\u610f\u7684\u8a5e\u8a9e\u3002\u7d9c\u4e0a\u6240\u8ff0\uff0c\u6211\u5011\u53ef\u4ee5(Torres-Moreno, 2014)\u4e4b\u793a\u4f8b\u7c21\u55ae\u63cf\u8ff0 RNN \u5c0d\u6bcf\u500b\u8a9e\u53e5\u9032\u884c\u6a19\u8a18\uff0c\u4e26\u4f7f\u7528\u9810\u6e2c\u51fa\u7684\u5206\u6578\u9032\u884c\u6392\u5e8f\uff0c\u9032\u800c\u5f97\u5230\u6700\u5f8c\u7684\u6458\u8981\u6210\u679c\u3002 \u4ef6\u7684\u4e3b\u65e8\uff0c\u800c\u4ee5\u6b64\u5f97\u5230\u7684\u6587\u4ef6\u5411\u91cf\u8868\u793a\u4e5f\u8f03\u80fd\u6db5\u84cb\u6587\u4ef6\u4e3b\u65e8\uff0c\u56e0\u800c\u80fd\u63d0\u5347\u6458\u8981\u7684\u6210\u6548\u3002 \u51fa\u7684 Pointer Network \u67b6\u69cb\uff0c\u7576\u6587\u4ef6\u4e2d\u6709\u5c08\u6709\u540d\u8a5e\u51fa\u73fe\u6642\uff0c\u4f46\u89e3\u78bc\u5668\u7684\u8a5e\u5178\u4e2d\u53ef\u80fd\u6c92\u6709\u8a72 \u6587\u4ef6\u4e2d\u7684\u5b8c\u6574\u6982\u5ff5\uff0c\u91cd\u65b0\u64b0\u5beb\u51fa\u6458\u8981\uff0c\u56e0\u6b64\u6458\u8981\u5167\u5bb9\u4e2d\u53ef\u80fd\u9084\u6709\u975e\u539f\u6587\u4ef6\u4e2d\u6240\u4f7f\u7528 \u7ae0\u904e\u9577\u6642\uff0c\u82e5\u55ae\u4f7f\u7528\u4e00\u500b RNN\uff0c\u5247\u6709\u53ef\u80fd\u6703\u907a\u5931\u6389\u8a31\u591a\u91cd\u8981\u7684\u8cc7\u8a0a\u3002\u6700\u5f8c\u900f\u904e\u53e6\u4e00\u500b et al., 2018b)\u63d0\u51fa\u5728\u6458\u8981\u65b9\u6cd5\u4e2d\u53c3\u8003\u6587\u4ef6\u7684\u6a19\u984c\u8cc7\u8a0a\uff0c\u53ef\u4ee5\u8b93\u6211\u5011\u7684\u65b9\u6cd5\u66f4\u5feb\u901f\u5730\u627e\u5230\u6587 \u662f\u5426\u8981\u751f\u6210\u65b0\u8a5e\u6216\u5f9e\u8f38\u5165\u6587\u4ef6\u8907\u88fd\uff0c\u6b64\u4e00\u6a5f\u5236\u662f\u53c3\u8003(Vinyals, Fortunato & Jaitly, 2015)\u63d0 10%\u7684\u6458\u8981\u6bd4\u4f8b\uff0c\u4e5f\u5c31\u662f\u6458\u8981\u9577\u5ea6\u70ba\u539f\u6587\u4ef6\u9577\u5ea6\u7684 10%\u3002\u800c\u91cd\u5beb\u5f0f\u6458\u8981\u4e3b\u8981\u6703\u4f9d\u539f \u6642\u9593\u9ede\u7684\u8f38\u51fa\u8996\u70ba\u6587\u4ef6\u7684\u5411\u91cf\u8868\u793a\u3002\u6b64\u4f5c\u6cd5\u5c0d\u65bc\u8f03\u9577\u7684\u6587\u7ae0\u800c\u8a00\u662f\u76f8\u7576\u6709\u6548\u7684\uff0c\u56e0\u70ba\u6587 \u4e9b\u76f8\u95dc\u7684\u984d\u5916\u8cc7\u8a0a\u8f14\u52a9\u8a13\u7df4\uff0c\u53ef\u4ee5\u8b93\u6211\u5011\u7684\u65b9\u6cd5\u66f4\u6df1\u5165\u5730\u5b78\u7fd2\u5230\u6587\u4ef6\u91cd\u8981\u8cc7\u8a0a\u3002(Narayan \u5982\uff1a\u8a5e\u6027\u3001\u8a5e\u983b\u7b49\uff1b\u7b2c\u4e8c\u7a2e\u5247\u662f\u5728\u89e3\u78bc\u5668\u751f\u6210\u8a5e\u5f59\u4e4b\u524d\uff0c\u52a0\u5165\u4e00\u500b\u63a7\u5236\u5668\uff0c\u63a7\u5236\u89e3\u78bc\u5668 \u7c21\u55ae\u7d44\u5408\u6210\u6458\u8981\u3002\u6458\u8981\u6bd4\u4f8b\u662f\u6307\u6458\u8981\u9577\u5ea6\u8207\u539f\u6587\u4ef6\u9577\u5ea6\u7684\u6bd4\u4f8b\uff0c\u4e00\u822c\u6211\u5011\u901a\u5e38\u9078\u7528 (Recurrent Neural Networks, RNNs)\uff0c\u5c07\u8a9e\u53e5\u5411\u91cf\u505a\u70ba\u6bcf\u500b\u6642\u9593\u9ede\u7684\u8f38\u5165\uff0c\u800c\u5c07\u6700\u5f8c\u4e00\u500b \u55ae\u55ae\u53ea\u8b93\u985e\u795e\u7d93\u7db2\u8def\u67b6\u69cb\u81ea\u52d5\u5b78\u7fd2\u8a9e\u53e5\u6216\u6587\u4ef6\u5411\u91cf\u8868\u793a\u7684\u6548\u679c\u4ecd\u6709\u9650\uff0c\u82e5\u80fd\u52a0\u5165\u4e00 \u984c\u3002\u9664\u4e86\u57fa\u672c\u67b6\u69cb\u5916\uff0c\u9084\u63d0\u51fa\u4e09\u7a2e\u6539\u826f\u7684\u7248\u672c\uff0c\u7b2c\u4e00\u7a2e\u662f\u5728\u8f38\u5165\u6642\u52a0\u5165\u4e00\u4e9b\u984d\u5916\u7684\u7279\u5fb5\uff0c \u5b9a\u4e4b\u6458\u8981\u6bd4\u4f8b(Summarization ratio)\uff0c\u5f9e\u539f\u6587\u4ef6\u4e2d\u9078\u51fa\u91cd\u8981\u6027\u9ad8\u7684\u8a9e\u53e5\u3001\u6bb5\u843d\u6216\u7ae0\u7bc0 \u662f\u53c3\u8003(Kim, 2014)\u7684\u65b9\u6cd5\uff0c\u4f7f\u7528 CNN \u8a08\u7b97\u8a9e\u53e5\u7684\u5411\u91cf\u8868\u793a\uff1b\u7b2c\u4e8c\u5c64\u70ba\u905e\u8ff4\u5f0f\u985e\u795e\u7d93\u7db2\u8def \u4ee5\u6b64\u6211\u5011\u53ef\u4ee5\u63a8\u8ad6\uff0c\u985e\u795e\u7d93\u7db2\u8def\u7684\u5b78\u7fd2\u4ecd\u9700\u4eba\u5de5\u7279\u5fb5\u8f14\u52a9\u65b9\u53ef\u66f4\u52a0\u63d0\u5347\u6210\u6548\u3002 \u7368\u7684\u89e3\u78bc\u7528\u8a5e\u5178\uff0c\u56e0\u6b64\u80fd\u5920\u8b93\u8a5e\u5178\u4e0d\u6703\u592a\u5927\uff0c\u540c\u6642\u53c8\u80fd\u5728\u8a13\u7df4\u7684\u6642\u5019\u6e1b\u5c11\u767c\u751f\u672a\u77e5\u8a5e\u554f \u7bc0\u9304\u5f0f\u6458\u8981\u8207\u91cd\u5beb\u5f0f\u6458\u8981\u4e4b\u5dee\u7570\u5728\u65bc\u5176\u7522\u751f\u6458\u8981\u7684\u539f\u7406\u4e0d\u540c\u3002\u7bc0\u9304\u5f0f\u6458\u8981\u662f\u4f9d\u64da\u56fa \u5c64\u5f0f\u7684\u7de8\u78bc\u5668\u6709\u5169\u5c64\uff0c\u7b2c\u4e00\u5c64\u70ba\u647a\u7a4d\u5f0f\u985e\u795e\u7d93\u7db2\u8def(Convolutional Neural Networks, CNNs)\uff0c \u4e0d\u932f\u3002\u4e0d\u904e\u5f9e\u5be6\u9a57\u5206\u6790\u53ef\u4ee5\u767c\u73fe\u5c0d\u65bc\u6458\u8981\u7d50\u679c\u6709\u8f03\u591a\u8ca2\u737b\u7684\u90e8\u5206\u5927\u591a\u5728\u65bc\u4eba\u5de5\u7279\u5fb5\u4e0a\uff0c 3.1 \u554f\u984c\u5b9a\u7fa9\u53ca\u5047\u8a2d (Problem Formulation)</td></tr></table>", | |
| "text": "Narayan et al., 2018b)\u4e3b\u8981\u7528\u7684\u57fa\u672c\u67b6\u69cb\u662f\u7531(Narayan et al., 2018a)\u8b8a\u5316\u800c\u6210\uff0c\u5dee\u7570\u5728 \u65bc\u5176\u5c07\u984d\u5916\u8cc7\u8a0a\u5411\u91cf\u8207\u8a9e\u53e5\u5411\u91cf\u5171\u540c\u7528\u65bc\u5224\u65b7\u662f\u5426\u70ba\u6458\u8981\u3002\u6b64\u65b9\u6cd5\u66f4\u662f\u9a57\u8b49\u985e\u795e\u7d93\u7db2\u8def \u67b6\u69cb\u6709\u984d\u5916\u8cc7\u8a0a\u8f14\u52a9\u80fd\u5b78\u7fd2\u66f4\u597d\u3002 \u8207\u6ce8\u610f\u529b\u6a5f\u5236\uff0c\u4ea6 \u7a31\u4e4b\u70ba\u5e8f\u5217\u5c0d\u5e8f\u5217\u6a21\u578b\uff0c\u4e26\u61c9\u7528\u65bc\u91cd\u5beb\u5f0f\u6458\u8981\u4efb\u52d9\u3002\u6ce8\u610f\u529b\u6a5f\u5236\u80fd\u8b93\u8f38\u5165\u6587\u4ef6\u5167\u5bb9\u8207\u8f38 \u51fa\u6458\u8981\u4e2d\u7684\u6587\u5b57\u4f5c\u4e00\u500b\u5c0d\u61c9\uff0c\u80fd\u627e\u5230\u6587\u4ef6\u8207\u6458\u8981\u4e2d\u8a5e\u5f59\u9593\u7684\u95dc\u4fc2\u3002(Rush et al., 2015) \u7684 \u67b6\u69cb\u8207 (Bahdanau et al., 2014) \u4e0d\u540c\u4e4b\u8655\u5728\u65bc\u5176\u4e26\u975e\u4f7f\u7528\u905e\u8ff4\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u4f5c\u70ba\u7de8\u78bc\u5668 \u8207\u89e3\u78bc\u5668\uff0c\u800c\u662f\u4f7f\u7528\u6700\u57fa\u672c\u7684\u524d\u5411\u5f0f\u985e\u795e\u7d93\u7db2\u8def (Feed-forward Neural Networks) \u7d50\u5408\u6ce8 \u610f\u529b\u6a5f\u5236\u4f5c\u70ba\u5176\u7de8\u78bc\u5668\uff0c\u800c\u89e3\u78bc\u5668\u5247\u662f\u57fa\u65bc(Bengio, Ducharme, Vincent & Jauvin, 2003) \u63d0\u51fa\u7684 NNLM \u8b8a\u5316\u3002\u6b64\u65b9\u6cd5\u5728\u8a9e\u53e5\u6458\u8981 (Sentence Summarization) \u4efb\u52d9\u4e0a\u5f97\u5230\u76f8\u7576\u512a\u7570 \u7684\u6210\u6548\uff0c\u56e0\u6b64\u4e5f\u8b49\u5be6\u985e\u795e\u7d93\u7db2\u8def\u80fd\u5920\u9069\u7528\u65bc\u91cd\u5beb\u5f0f\u6458\u8981\u4efb\u52d9\u4e0a\u3002 \u96a8\u8457\u6df1\u5c64\u5b78\u7fd2\u7684\u5feb\u901f\u767c\u5c55\uff0c\u905e\u8ff4\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u5728\u5e8f\u5217\u76f8\u95dc\u4efb\u52d9\u4e0a\u7684\u6210\u529f\u4ea6\u6f38\u6f38\u5ee3\u70ba \u4eba\u77e5\uff0c\u56e0\u6b64(Chopra et al., 2016) \u5247\u63d0\u51fa\u4e00\u500b\u905e\u8ff4\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u7684\u7de8\u78bc\u89e3\u78bc\u5668\u67b6\u69cb\uff0c\u61c9\u7528 \u65bc\u8a9e\u53e5\u6458\u8981\u4efb\u52d9\u4e0a\u3002\u6b64\u65b9\u6cd5\u4e3b\u8981\u662f (Rush et al., 2015) \u7684\u5ef6\u4f38\uff0c\u5176\u7de8\u78bc\u5668\u4f7f\u7528\u647a\u7a4d\u5f0f\u985e\u795e \u7d93\u7db2\u8def\uff0c\u800c\u89e3\u78bc\u5668\u5247\u4f7f\u7528\u9577\u77ed\u671f\u8a18\u61b6 (Long Short-Term Memory, LSTM) (Hochreiter & Schmidhuber, 1997) \u55ae\u5143\u4f5c\u70ba\u905e\u8ff4\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u7684\u57fa\u672c\u55ae\u5143\u3002LSTM \u662f \u905e\u8ff4\u5f0f\u985e\u795e\u7d93\u7db2 \u8def\u6f14\u8b8a\u7684\u67b6\u69cb\uff0c\u56e0\u5176\u5177\u6709\u4e09\u500b\u9598\u9580: \u8f38\u5165\u9598 (input gate)\u3001\u907a\u5fd8\u9598 (forget gate) \u53ca\u8f38\u51fa\u9598 \u5289\u6148\u6069 \u7b49 \u63d0\u51fa\u7684 Gated Recurrent Unit (GRU) \u800c\u975e LSTM\uff0cGRU \u540c\u6a23\u5177\u6709\u9598\u9580\uff0c\u4f46\u662f\u50c5\u6709\u5169\u500b\uff0c \u4e14\u6c92\u6709\u984d\u5916\u7684\u8a18\u61b6\u55ae\u5143\uff0c\u4f46\u662f\u6574\u9ad4\u7684\u8a18\u61b6\u6548\u679c\u662f\u4e00\u6a23\u7684\uff0c\u8a13\u7df4\u53c3\u6578\u91cf\u6e1b\u5c11\u5f88\u591a\uff0c\u53ef\u4ee5\u6bd4 LSTM \u66f4\u5feb\u901f\u5730\u5efa\u69cb\u548c\u8a13\u7df4\u3002(Nallapati et al., 2016) \u4e2d\u63d0\u5230\u5728\u8a9e\u8a00\u751f\u6210\u6642\u6703\u9047\u5230\u672a\u77e5\u8a5e (Out-of-vocabulary, OOV) \u554f\u984c\uff0c\u70ba\u4e86\u89e3\u6c7a\u6b64\u554f\u984c\uff0c\u52a0\u5165 Large Vocabulary Trick (LVT)(Jean, Cho, Memisevic & Bengio, 2014)\uff0c\u6b64\u6280\u8853\u662f\u5c0d\u6bcf\u5c0f\u6279 (mini-batch) \u8a13\u7df4\u8cc7\u6599\u5efa\u7acb\u55ae", | |
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| "content": "<table><tr><td/><td/><td>\u5289\u6148\u6069 \u7b49</td></tr><tr><td colspan=\"3\">\u57fa\u672c\u67b6\u69cb\u4e2d\u5305\u542b\u4e00\u968e\u5c64\u5f0f\u7de8\u78bc\u5668\u53ca\u4e00\u89e3\u78bc\u5668\uff0c\u4ea6\u7a31\u4e4b\u70ba\u8a9e\u53e5\u9078\u53d6\u5668\u3002\u968e\u5c64\u5f0f\u7de8\u78bc\u5668\u4e2d\u4e3b</td></tr><tr><td colspan=\"3\">\u8981\u6709\u5169\u500b\u968e\u5c64\uff0c\u6211\u5011\u6703\u5148\u91dd\u5c0d\u6587\u4ef6\u4e2d\u7684\u8a9e\u53e5\u627e\u5230\u5c0d\u61c9\u7684\u8a9e\u53e5\u8868\u793a\uff0c\u518d\u5f9e\u8a9e\u53e5\u8868\u793a\u4e2d\u5b78\u7fd2</td></tr><tr><td colspan=\"3\">\u5230\u6587\u4ef6\u4e2d\u7684\u91cd\u8981\u6982\u5ff5\uff0c\u4ea6\u53ef\u7a31\u70ba\u6587\u4ef6\u8868\u793a\uff1b\u6700\u5f8c\u6703\u5c07\u8a9e\u53e5\u8868\u793a\u53ca\u6587\u4ef6\u8868\u793a\u7686\u653e\u7f6e\u65bc\u8a9e\u53e5</td></tr><tr><td colspan=\"2\">\u9078\u53d6\u5668\u4e2d\uff0c\u4f7f\u5176\u80fd\u5920\u6839\u64da\u6587\u4ef6\u8868\u793a\u53ca\u8a9e\u53e5\u8868\u793a\uff0c\u8fa8\u5225\u53ca\u6392\u5e8f\u6458\u8981\u53e5\u3002</td><td/></tr><tr><td colspan=\"2\">3.2.1 \u8a9e\u53e5\u7de8\u78bc\u5668 (Sentence Encoder)</td><td/></tr><tr><td colspan=\"3\">\u6211\u5011\u5229\u7528\u647a\u7a4d\u5f0f\u985e\u795e\u7d93\u7db2\u8def (Convolutional Neural Networks, CNNs) \u5c07\u6bcf\u500b\u4e0d\u540c\u9577\u5ea6\u7684</td></tr><tr><td colspan=\"3\">\u8a9e\u53e5\u6295\u5f71\u81f3\u5411\u91cf\u7a7a\u9593\uff0c\u80fd\u5920\u5f97\u5230\u56fa\u5b9a\u9577\u5ea6\u7684\u5411\u91cf\u8868\u793a (Representation)\u3002\u5728\u904e\u53bb\u7684\u7814\u7a76\u4e2d</td></tr><tr><td colspan=\"3\">\u986f\u793a\uff0cCNNs \u5728 NLP \u9818\u57df\u7684\u4efb\u52d9\u4e2d\u6709\u76f8\u7576\u4e0d\u932f\u7684\u6210\u6548(Cheng & Lapata, 2016; Collobort</td></tr><tr><td colspan=\"3\">et al., 2011; Kalchbrenner, Grefenstette & Blunsom, 2014; Kim, Jernite, Sontag & Rush, 2016;</td></tr><tr><td colspan=\"3\">Lei, Barzilay & Jaakkola, 2015; Zhang, Zhao & LeCun, 2015)\u3002\u6211\u5011\u4f7f\u7528 1-D \u647a\u7a4d</td></tr><tr><td>(Convolution) \u4e26\u7d66\u5b9a\u5bec\u5ea6</td><td>\u7684\u647a\u7a4d\u6838 (Kernel) \uff0c\u5176\u5b9a\u7fa9\u70ba\u6bcf\u6b21\u770b</td><td>\u500b\u8a5e\u5f59\uff0c\u985e\u4f3c\u65bc</td></tr><tr><td colspan=\"3\">N \u5143\u6a21\u578b (N-gram) \u7684\u6982\u5ff5\uff0c\u53ef\u5f97\u5230\u7279\u5fb5\u5716 (Feature map) \u3002\u4e4b\u5f8c\uff0c\u5c0d\u6bcf\u500b\u7279\u5fb5\u5716\u6cbf\u8457</td></tr><tr><td colspan=\"3\">\u6642\u5e8f\u4f7f\u7528\u6700\u5927\u6c60\u5316 (Max Pooling)\uff0c\u5c07\u7279\u5fb5\u5716\u4e2d\u7684\u6700\u5927\u503c\u8996\u70ba\u8a9e\u53e5\u7279\u5fb5\u3002\u70ba\u4e86\u80fd\u627e\u5230\u66f4\u597d</td></tr><tr><td colspan=\"3\">\u7684\u7279\u5fb5\uff0c\u6211\u5011\u4f7f\u7528\u591a\u7a2e\u5bec\u5ea6\u7684\u647a\u7a4d\u6838\uff0c\u4e14\u6bcf\u7a2e\u5bec\u5ea6\u6709\u591a\u500b\u4e0d\u540c\u7684\u647a\u7a4d\u6838\uff0c\u6700\u5f8c\u5c07\u6240\u5f97\u5230</td></tr><tr><td colspan=\"2\">\u5c0d\u65bc\u6bcf\u500b\u8a9e\u97f3\u6587\u4ef6\uff0c\u6211\u5011\u5b9a\u7fa9\u4ee5\u4e0b\u5e7e\u9ede\u5047\u8a2d\uff1a \u7684\u7279\u5fb5\u4e32\u63a5\u5728\u4e00\u8d77\uff0c\u5373\u70ba\u8a9e\u53e5\u7684\u5411\u91cf\u8868\u793a\u3002</td><td/></tr><tr><td colspan=\"3\">\uf0b7 \u8a9e\u97f3\u8cc7\u8a0a\u53ef\u900f\u904e\u984d\u5916\u7684\u8072\u5b78\u7279\u5fb5\u53c3\u8003\u9032\u6a21\u578b\u8a13\u7df4 3.2.2 \u6587\u4ef6\u7de8\u78bc\u5668 (Document Encoder) \uf0b7 \u4f7f\u7528\u5b57\u5411\u91cf\u53ef\u6709\u6548\u6539\u5584\u8a9e\u53e5\u8868\u793a\u7684\u6210\u6548\u4e26\u62b5\u92b7\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4 \u5728\u6587\u4ef6\u7de8\u78bc\u5668\u4e2d\uff0c\u6211\u5011\u4f7f\u7528\u905e\u8ff4\u5f0f\u985e\u795e\u7d93\u7db2\u8def (Recurrent Neural Networks, RNNs)\uff0c\u5c07\u6bcf \uf0b7 \u6458\u8981\u53e5\u53ef\u88ab\u5176\u4ed6\u975e\u6458\u8981\u53e5\u89e3\u91cb \u500b\u6587\u4ef6\u7684\u8a9e\u53e5\u5e8f\u5217\u8f49\u63db\u6210\u4e00\u56fa\u5b9a\u9577\u5ea6\u4e4b\u5411\u91cf\u8868\u793a\uff0c\u5176\u80fd\u5920\u64f7\u53d6\u5230\u6587\u4ef6\u4e2d\u7684\u91cd\u8981\u8cc7\u8a0a\u3002\u5176</td></tr><tr><td colspan=\"3\">\uf0b7 \u5f37\u5316\u5b78\u7fd2\u6280\u8853\u53ef\u8a13\u7df4\u6458\u8981\u4e4b\u6392\u5e8f \u4e2d\u70ba\u4e86\u907f\u514d\u7522\u751f\u6d88\u5931\u7684\u68af\u5ea6 (Vanishing Gradient) \u554f\u984c\uff0c\u6211\u5011\u9078\u64c7\u4f7f\u7528 GRU (Gated</td></tr><tr><td colspan=\"3\">\u5f8c\u7e8c\u6211\u5011\u6703\u91dd\u5c0d\u4e0a\u8ff0\u4e4b\u5047\u8a2d\u5c0d\u6a21\u578b\u67b6\u69cb\u9032\u884c\u4e0d\u540c\u7684\u6539\u9032\uff0c\u4e14\u6703\u8a73\u7d30\u95e1\u8ff0\u5176\u52d5\u6a5f\u3002 Recurrent Unit) (</td></tr><tr><td colspan=\"2\">3.2 \u57fa\u672c\u67b6\u69cb (Basic Architecture)</td><td/></tr><tr><td/><td>[Figure 2. Basic architecture]</td><td/></tr></table>", | |
| "text": "Cho et al., 2014) \u4f5c\u70ba RNN \u7684\u57fa\u672c\u55ae\u5143\u3002\u6b64\u5916\uff0c\u6211\u5011\u53c3\u8003\u76f8\u95dc\u5be6\u4f5c\uff0c\u5c07 \u6587\u4ef6\u4ee5\u5012\u5e8f\u7684\u65b9\u5f0f\u4f5c\u70ba\u8f38\u5165", | |
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| "content": "<table><tr><td/><td/><td>\u5289\u6148\u6069 \u7b49</td></tr><tr><td colspan=\"3\">\u53e5\u6703\u6709\u5c0d\u61c9\u7684\u8072\u5b78\u7279\u5fb5\uff0c\u56e0\u6b64\u4ee4\u8072\u5b78\u7279\u5fb5\u5411\u91cf\u70ba \uff0c\u6211\u5011\u7684\u65b9\u6cd5\u53ef\u5b9a\u7fa9\u4e0b\u5217\u65b9\u7a0b\u5f0f\uff1a</td></tr><tr><td>\u2032</td><td>, ;</td></tr><tr><td/><td>,</td><td>\u5c07\u6bcf\u500b\u8a9e\u53e5</td></tr><tr><td colspan=\"2\">\u9032\u884c\u6392\u5e8f\uff0c\u4f9d\u7167\u56fa\u5b9a\u7684\u6458\u8981\u6bd4\u4f8b\u9078\u53d6\u6392\u540d\u9ad8\u7684\u8a9e\u53e5\u4f5c\u70ba\u5b8c\u6574\u7684\u6458\u8981\u7d50\u679c\u3002</td></tr><tr><td colspan=\"3\">\u5716 3. \u70ba\u4e86\u80fd\u5920\u907f\u514d\u6458\u8981\u7d50\u679c\u53d7\u5230\u8fa8\u8b58\u932f\u8aa4\u7684\u5f71\u97ff\uff0c\u6211\u5011\u8a8d\u70ba\u8072\u5b78\u7279\u5fb5\u80fd\u5920\u4fdd\u7559\u6bcf\u500b\u6587\u4ef6\u7684\u8a9e</td></tr><tr><td colspan=\"3\">\u97f3\u8cc7\u8a0a\u4e14\u4e0d\u53d7\u8fa8\u8b58\u932f\u8aa4\u4e4b\u5f71\u97ff\uff0c\u56e0\u6b64\u63d0\u51fa\u4e09\u7a2e\u65b9\u5f0f\u5c07\u8072\u5b78\u7279\u5fb5\u8207\u4e0a\u8ff0\u67b6\u69cb\u7d50\u5408\uff0c\u4f7f\u5f97\u5728</td></tr><tr><td colspan=\"3\">\u5224\u65b7\u6458\u8981\u7684\u6642\u5019\u80fd\u5920\u53c3\u8003\uff0c\u4ee5\u5f97\u5230\u66f4\u597d\u7684\u6458\u8981\u6210\u679c\u3002\u8072\u5b78\u7279\u5fb5\u662f\u4ee5\u8a9e\u53e5\u70ba\u55ae\u4f4d\uff0c\u6bcf\u500b\u8a9e</td></tr></table>", | |
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| "TABREF7": { | |
| "num": null, | |
| "content": "<table><tr><td>\u57fa\u65bc\u7aef\u5c0d\u7aef\u6a21\u578b\u5316\u6280\u8853\u4e4b\u8a9e\u97f3\u6587\u4ef6\u6458\u8981 \u57fa\u65bc\u7aef\u5c0d\u7aef\u6a21\u578b\u5316\u6280\u8853\u4e4b\u8a9e\u97f3\u6587\u4ef6\u6458\u8981 \u57fa\u65bc\u7aef\u5c0d\u7aef\u6a21\u578b\u5316\u6280\u8853\u4e4b\u8a9e\u97f3\u6587\u4ef6\u6458\u8981 \u57fa\u65bc\u7aef\u5c0d\u7aef\u6a21\u578b\u5316\u6280\u8853\u4e4b\u8a9e\u97f3\u6587\u4ef6\u6458\u8981</td><td>45 \u5289\u6148\u6069 \u7b49 47 \u5289\u6148\u6069 \u7b49 49 \u5289\u6148\u6069 \u7b49 51 \u5289\u6148\u6069 \u7b49</td></tr><tr><td colspan=\"2\">One-class classification \u8cc7\u6599\u4ea6\u5206\u6210\u5169\u7a2e\uff0cTD \u70ba\u7d93\u904e\u4eba\u5de5\u6a19\u8a3b\u7684\u6587\u4ef6\uff0c\u800c SD \u5247\u70ba\u7d93\u904e\u81ea\u52d5\u8a9e\u97f3\u8fa8\u8b58\u5f8c\u7522\u751f\u7684\u6587 Sentences in summary Other sentences \u4ef6\uff0c\u56e0\u6b64 SD \u6703\u6709\u90e8\u5206\u7684\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u3002\u8868 1 \u662f\u5c0d\u8a13\u7df4\u96c6\u53ca\u6e2c\u8a66\u96c6\u4f5c\u7684\u4e00\u4e9b\u57fa\u672c\u7d71\u8a08\u8cc7 \u6599\u3002\u6b64\u5916\uff0c\u8a9e\u97f3\u6587\u4ef6\u7684\u8072\u5b78\u7279\u5fb5\u985e\u578b\u5217\u65bc\u8868 2 \u4e2d\uff0c\u662f\u5229\u7528 Praat \u5de5\u5177\u64f7\u53d6\u7684\u7d50\u679c\uff0c\u7e3d\u8a08\u6709 36 \u500b\u7279\u5fb5\u3002 \u8868 1. \u7528\u65bc\u6458\u8981\u4e4b\u5ee3\u64ad\u65b0\u805e\u6587\u4ef6\u7684\u7d71\u8a08\u8cc7\u8a0a[Tsai et al., 2016] [Table 1. The statistics of MATBN] \u8a13\u7df4\u96c6 \u6e2c\u8a66\u96c6 \u6587\u4ef6\u6578 185 20 \u6bcf\u6587\u4ef6\u5e73\u5747\u53e5\u6578 20 23.3 \u6bcf\u53e5\u5e73\u5747\u8a5e\u6578 17.5 16.9 \u6bcf\u6587\u4ef6\u5e73\u5747\u8a5e\u6578 326.0 290.3 \u5e73\u5747\u8a5e\u932f\u8aa4\u7387 38.0% 39.4% \u5e73\u5747\u5b57\u932f\u8aa4\u7387 28.8% 29.8% \u6b64\u5916\uff0c\u6211\u5011\u6240\u4f7f\u7528\u4e4b\u8072\u5b78\u7279\u5fb5\u5217\u65bc\u8868 2 \u4e2d\uff0c\u662f\u5229\u7528 Praat \u5de5\u5177\u64f7\u53d6\u7684\u7d50\u679c\uff0c\u7e3d\u8a08\u6709 36 \u500b\u7279\u5fb5\uff0c\u53ef\u7c21\u55ae\u5206\u70ba\u56db\u7a2e\u985e\u578b\u4ecb\u7d39\uff1a \uf0b7 Pitch \u97f3\u9ad8\uff1a \u7576\u6211\u5011\u5728\u8aaa\u8a71\u6642\uff0c\u8b1b\u5230\u91cd\u9ede\u7684\u6642\u5019\uff0c\u97f3\u9ad8\u5c31\u6703\u6bd4\u8f03\u9ad8\u4f86\u5438\u5f15\u6ce8\u610f\uff0c\u53cd\u4e4b\u5247\u6703\u7dad\u6301\u76f8 \u5c0d\u8f03\u4f4e\u7684\u97f3\u9ad8\u3002 \uf0b7 Energy \u80fd\u91cf\uff1a \u80fd\u91cf\u4e00\u822c\u662f\u6307\u8a9e\u8005\u7684\u8aaa\u8a71\u97f3\u91cf\uff0c\u901a\u5e38\u90fd\u6703\u88ab\u8996\u70ba\u4e00\u9805\u91cd\u8981\u7684\u8cc7\u8a0a\u3002\u7576\u6211\u5011\u8981\u5f37\u8abf\u67d0 \u4ef6\u4e8b\u60c5\u6642\uff0c\u9664\u4e86\u97f3\u9ad8\u6703\u63d0\u9ad8\u5916\uff0c\u97f3\u91cf\u4e5f\u6703\u81ea\u7136\u5730\u653e\u5927\uff0c\u56e0\u800c\u80fd\u5e6b\u52a9\u6a21\u578b\u5206\u8fa8\u91cd\u8981\u8cc7 \u8a0a\u3002 \uf0b7 Duration \u6301\u7e8c\u6642\u9593\uff1a \u6301\u7e8c\u6642\u9593\u6709\u9ede\u985e\u4f3c\u65bc\u4e00\u500b\u8a9e\u53e5\u4e2d\u7684\u8a5e\u5f59\u6578\u91cf\uff0c\u7576\u6301\u7e8c\u6642\u9593\u8d8a\u9577\u6c92\u6709\u9593\u65b7\u6642\uff0c\u5247\u8868\u793a \u9019\u53e5\u8a71\u5305\u542b\u7684\u8cc7\u8a0a\u76f8\u5c0d\u8f03\u591a\u3002 \uf0b7 Peak and Formant \u5cf0\u8207\u5171\u632f\u5cf0\uff1a \u8a13\u7df4\u8a5e\u5411\u91cf\u7684\u5dee\u7570\u5176\u5be6\u4e0d\u5927\uff0c\u56e0\u6b64\u5728\u6574\u9ad4\u7684\u6458\u8981\u6548\u679c\u4e0a\u5169\u8005\u7684\u5dee\u7570\u5176\u5be6\u4e26\u6c92\u6709\u5f88\u5927\uff0c\u4f46 \u8868 5. \u968e\u5c64\u5f0f\u985e\u795e\u7d93\u6458\u8981\u6a21\u578b-\u5f37\u5316\u5b78\u7fd2 \u9593\u7684\u95dc\u806f\u6027\uff0c\u800c\u5c0d\u65bc\u8a9e\u97f3\u6587\u4ef6\u800c\u8a00\uff0c\u82e5\u8fa8\u8b58\u932f\u8aa4\u7684\u592a\u591a\uff0c\u6bd4\u8f03\u96e3\u627e\u5230\u8a9e\u53e5\u9593\u7684\u8a9e\u610f\u95dc\u806f \u6027\uff0c\u56e0\u6b64\u7576\u5169\u8005\u540c\u6642\u8a13\u7df4\u6642\uff0c\u96d6\u7136\u90fd\u662f\u91dd\u5c0d\u8cc7\u8a0a\u6027\uff0c\u4f46\u53ef\u80fd\u56e0\u70ba\u592a\u904e\u6ce8\u91cd\u800c\u9020\u6210\u53cd\u6548\u679c\u3002 \u73fe\u6ce8\u610f\u529b\u6a5f\u5236\u548c\u5f37\u5316\u5b78\u7fd2\u7686\u53ef\u63d0\u5347\u6458\u8981\u8cc7\u8a0a\u6027\uff0c\u4f46\u540c\u6642\u4f7f\u7528\u6642\u6548\u679c\u6703\u76f8\u5c0d\u8f03\u5dee\uff1b\u5176\u6b21\u5728 \u8868 2\u8072\u5b78\u7279\u5fb5 CBOW \u76f8\u8f03\u65bc SG \u662f\u8f03\u512a\u7570\u7684\uff0c\u800c\u6b64\u4e8c\u8005\u65b9\u6cd5\u7684\u6548\u80fd\u4ea6\u8d85\u8d8a\u50b3\u7d71\u7684\u5411\u91cf\u6a21\u578b\u8a31\u591a\u3002 [Table 5. Results of our model with reinforcement learning] \u6027\uff0c\u56e0\u800c\u5c0e\u81f4\u7d50\u679c\u76f8\u5c0d\u8f03\u5dee\u3002 \u907f\u514d\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u7684\u90e8\u5206\uff0c\u6b21\u8a5e\u5411\u91cf\u8207\u8072\u5b78\u7279\u5fb5\u7686\u6709\u4e0d\u932f\u7684\u6210\u6548\uff0c\u5c24\u4ee5\u6b21\u8a5e\u5411\u91cf\u7684\u6548\u679c \u5176\u6b21\uff0c\u6211\u5011\u4e5f\u5617\u8a66\u7d50\u5408\u6ce8\u610f\u529b\u6a5f\u5236\u548c\u8072\u5b78\u7279\u5fb5\u7684\u61c9\u7528\uff0c\u5982\u8868 9 \u7684\u6700\u5f8c\u5169\u5217\uff0c\u7531\u65bc\u524d 1. Pitch (min, max, diff, avg) \u6700\u5f8c\u6211\u5011\u91dd\u5c0d\u76e3\u7763\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u67b6\u69cb\u4f5c\u8a0e\u8ad6\uff0cDNN \u662f\u6700\u57fa\u672c\u7684\u591a\u5c64\u985e\u795e\u7d93\u7db2\u8def\u67b6\u69cb\uff0c \u6587\u5b57\u6587\u4ef6 \u8a9e\u97f3\u6587\u4ef6 \u8868 7. \u968e\u5c64\u5f0f\u985e\u795e\u7d93\u6458\u8981\u6a21\u578b-\u6b21\u8a5e\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236 \u8f03\u70ba\u986f\u8457\uff1b\u6700\u5f8c\u5c0d\u65bc\u6458\u8981\u9023\u8cab\u6027\uff0c\u6211\u5011\u7684\u65b9\u6cd5\u96d6\u7136\u6709\u5b78\u7fd2\u6392\u5e8f\uff0c\u4f46\u8cc7\u6599\u96c6\u4e2d\u7684\u53c3\u8003\u6458\u8981 \u9762\u7684\u8a0e\u8ad6\u4e2d\u767c\u73fe\u4f7f\u7528\u5c40\u90e8\u5411\u91cf\u65b9\u5f0f\u7d50\u5408\u8072\u5b78\u7279\u5fb5\u5728\u8a9e\u97f3\u6587\u4ef6\u4e0a\u6703\u6709\u8f03\u4f73\u7684\u6548\u679c\uff0c\u56e0\u6b64\u6b64 2. Peak normalized cross-correlation of pitch (min, max, diff, avg) \u800c CNN \u5247\u662f\u4f7f\u7528\u647a\u7a4d\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u67b6\u69cb\uff0cRefresh \u662f\u8207\u672c\u8ad6\u6587\u76f8\u4f3c\u7684\u968e\u5c64\u5f0f\u67b6\u69cb\u3002\u5176\u4e2d ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L [Table 7. Results of our model with sub-word information and attention mechanism] \u4e0d\u5305\u542b\u6392\u5e8f\u8cc7\u8a0a\uff0c\u56e0\u6b64\u7121\u6cd5\u5b8c\u6574\u5730\u5b78\u7fd2\u5230\u8a9e\u53e5\u9593\u7684\u9023\u8cab\u6027\u3002\u56e0\u6b64\u900f\u904e\u521d\u6b65\u7684\u5be6\u9a57\u7d50\u679c\uff0c \u90e8\u5206\u5be6\u9a57\u4ea6\u63a1\u7528\u5c40\u90e8\u5411\u91cf\u3002\u5be6\u9a57\u7d50\u679c\u986f\u793a\u52a0\u5165\u8072\u5b78\u7279\u5fb5\u5728\u6587\u5b57\u6587\u4ef6\u6458\u8981\u4e0a\u6709\u4e9b\u8a31\u7684\u63d0\u5347\uff0c 3. Energy value (min, max, diff, avg) 4. Duration value (min, max, diff, avg) 5. 1 st formant value (min, max, diff, avg) 6. 2 nd formant value (min, max, diff, avg) 7. 3 rd formant value (min, max, diff, avg) 4.2 \u5be6\u9a57\u7d50\u679c (Results) \u9996\u5148\u672c\u8ad6\u6587\u5148\u6bd4\u8f03\u904e\u53bb\u7684\u6458\u8981\u65b9\u6cd5\u65bc\u6211\u5011\u7684\u8cc7\u6599\u96c6\u4e0a\u7684\u6210\u6548\uff0c\u4e4b\u5f8c\u5728\u91dd\u5c0d\u6211\u5011\u63d0\u51fa\u7684\u67b6 \u69cb\u548c\u4e0d\u540c\u7d44\u5408\u7684\u6458\u8981\u6210\u679c\u5dee\u7570\u3002 4.2.1 \u57fa\u790e\u5be6\u9a57(Baseline) \u904e\u53bb MATBN \u8cc7\u6599\u96c6\u66fe\u61c9\u7528\u5728\u5404\u7a2e\u4e0d\u540c\u7684\u6458\u8981\u65b9\u6cd5\u4e0a\uff0c\u5f9e\u50b3\u7d71\u7684\u6458\u8981\u65b9\u6cd5 (VSM, LSA) \u3001 \u975e\u76e3\u7763\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u67b6\u69cb(SG, CBOW)\u5230\u76e3\u7763\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u67b6\u69cb(DNN, CNN)\u90fd\u66fe\u6709 \u5b78\u8005\u4f7f\u7528\u3002\u56e0\u6b64\u6211\u5011\u5c07\u904e\u53bb\u7684\u7814\u7a76\u8868\u73fe\u4f5c\u70ba\u672c\u8ad6\u6587\u6bd4\u8f03\u7684\u57fa\u790e\u5be6\u9a57\uff0c\u7d50\u679c\u5217\u65bc\u8868 3 \u4e2d\u3002 \u8868 3. \u57fa\u790e\u5be6\u9a57\u7d50\u679c [Table 3. Results of baseline] \u6587\u5b57\u6587\u4ef6 \u8a9e\u97f3\u6587\u4ef6 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L 0.347 0.228 0.290 0.342 0.189 0.287 0.362 0.233 0.316 0.345 0.201 0.301 0.410 0.300 0.364 0.378 0.239 0.333 CBOW VSM LSA SG 0.415 0.308 0.366 0.393 0.250 0.349 DNN 0.488 0.382 0.444 0.371 0.233 0.332 CNN 0.501 0.407 0.460 0.370 0.208 0.312 \u5728\u6587\u5b57\u6587\u4ef6\u7684\u6548\u679c\u4e0a\uff0c\u53ef\u4ee5\u5f88\u660e\u986f\u5730\u767c\u73fe\u4e09\u8005\u90fd\u8d85\u8d8a\u4e86\u975e\u76e3\u7763\u5f0f\u7684\u65b9\u6cd5\uff0c\u5c24\u4ee5 CNN \u7684 \u6548\u679c\u6700\u597d\uff0c\u53ef\u80fd\u662f\u56e0\u70ba CNN \u6bd4 DNN \u66f4\u80fd\u6293\u5230\u91cd\u8981\u8cc7\u8a0a\uff0c\u800c\u53c3\u6578\u91cf\u53c8\u6bd4 Refresh \u5c11\uff0c\u8f03 \u6613\u65bc\u8a13\u7df4\uff1b\u4f46\u5728\u8a9e\u97f3\u6587\u4ef6\u7684\u6210\u6548\u4e0a\uff0c\u4e09\u8005\u90fd\u6bd4\u975e\u76e3\u7763\u5f0f\u7684\u6548\u679c\u5dee\uff0c\u53ef\u80fd\u662f\u56e0\u70ba\u5176\u592a\u904e\u65bc \u4f9d\u8cf4\u6587\u4ef6\u4e2d\u7684\u8a5e\u5f59\u8cc7\u8a0a\uff0c\u56e0\u800c\u53d7\u5230\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u7684\u5f71\u97ff\u8f03\u70ba\u56b4\u91cd\uff0c\u5c0e\u81f4\u5176\u6548\u679c\u8f03\u5dee\u3002 \u5f8c\u7e8c\u7ae0\u7bc0\u6211\u5011\u5c07\u4ee5 Refresh \u7684\u6578\u64da\u8207\u672c\u8ad6\u6587\u63d0\u51fa\u4e4b\u67b6\u69cb\u9032\u884c\u6bd4\u8f03\u53ca\u5206\u6790\u3002 4.2.2 \u968e\u5c64\u5f0f\u985e\u795e\u7d93\u6458\u8981\u6a21\u578b\u5be6\u9a57 (Our models) \u5728\u5be6\u9a57\u7d50\u679c\u5206\u6790\u4e2d\uff0c\u6211\u5011\u524d\u9762\u7ae0\u7bc0\u4ecb\u7d39\u6a21\u578b\u6642\u63d0\u5230\u7684\u526f\u67b6\u69cb\u5206\u958b\u5be6\u9a57\uff0c\u4ee5\u4e0b\u6703\u5217\u51fa\u4e0d\u540c \u5be6\u9a57\u8a2d\u7f6e\u7684\u6548\u679c\uff0c\u4ee5\u53ca\u7d50\u679c\u8a0e\u8ad6\u8207\u5206\u6790\u3002 I. \u6b21\u8a5e\u5411\u91cf \u9996\u5148\uff0c\u6211\u5011\u5148\u6bd4\u8f03\u8a5e\u5411\u91cf\u548c\u5b57\u5411\u91cf\u7528\u65bc\u6a21\u578b\u4e2d\u7684\u6548\u679c\uff0c\u5982\u4e0b\u8868\u6240\u793a\uff0c\u53ef\u4ee5\u770b\u51fa\u55ae\u7368\u4f7f\u7528 \u8a5e\u5411\u91cf\u7684\u7d50\u679c\u5728\u8a9e\u97f3\u6587\u4ef6\u4e0a\u7684\u6548\u679c\u53cd\u800c\u6bd4\u55ae\u7368\u4f7f\u7528\u5b57\u5411\u91cf\u7684\u6642\u5019\u512a\u7570\uff0c\u4f46\u5728\u6587\u5b57\u6587\u4ef6\u4e0a \u53cd\u800c\u76f8\u53cd\uff0c\u9019\u6a23\u7684\u7d50\u679c\u8207\u6211\u5011\u7684\u5047\u8a2d\u6709\u4e9b\u8a31\u51fa\u5165\uff0c\u53ef\u80fd\u662f\u56e0\u70ba\u8a13\u7df4\u6587\u4ef6\u4e2d\u932f\u8aa4\u7684\u5b57\u6bd4\u8f03 \u96c6\u4e2d\uff0c\u56e0\u800c\u7121\u6cd5\u900f\u904e\u5468\u570d\u7684\u8cc7\u8a0a\u4f86\u5b78\u7fd2\u6b63\u78ba\u7684\u8a5e\u5f59\u8cc7\u8a0a\uff1b\u6b64\u5916\uff0c\u82e5\u4f7f\u7528\u878d\u5408\u5411\u91cf\u65bc\u6211\u5011 \u7684\u6a21\u578b\u4e2d\uff0c\u5728\u8a9e\u97f3\u6587\u4ef6\u7684\u7d50\u679c\u4e0a\u53ef\u4ee5\u6709\u5f88\u660e\u986f\u7684\u9032\u6b65\uff0c\u4f46\u5728\u6587\u5b57\u6587\u4ef6\u4e0a\u50c5\u65bc ROUGE-2 \u6709\u9032\u6b65\uff0c\u56e0\u800c\u6211\u5011\u8a8d\u70ba\u5b57\u5411\u91cf\u548c\u8a5e\u5411\u91cf\u4e4b\u9593\u53ef\u80fd\u4ecd\u6709\u76f8\u8f14\u76f8\u6210\u7684\u4f5c\u7528\u3002 \u8868 4. \u968e\u5c64\u5f0f\u985e\u795e\u7d93\u6458\u8981\u6a21\u578b-\u6b21\u8a5e\u5411\u91cf\u7d50\u679c \u6587\u5b57\u6587\u4ef6 \u8a9e\u97f3\u6587\u4ef6 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L Refresh [Narayan et al., 2018a] 0.453 0.372 0.446 0.329 0.197 0.319 \u8a5e\u5411\u91cf 0.526 0.473 0.520 0.380 0.262 0.370 \u5b57\u5411\u91cf 0.544 0.473 0.535 0.363 0.242 0.351 Refresh [Narayan et al., 2018a] 0.453 0.372 0.446 0.329 0.197 \u6587\u5b57\u6587\u4ef6 \u8a9e\u97f3\u6587\u4ef6 \u8db3\u4ee5\u8b49\u660e\u6211\u5011\u63d0\u51fa\u7684\u67b6\u69cb\u5c0d\u65bc\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u6709\u4e0d\u932f\u7684\u6210\u6548\uff0c\u4f46\u4e3b\u8981\u90fd\u53cd\u61c9\u65bc\u6587\u5b57\u5167\u5bb9\u4e0a\uff0c \u4f46\u65bc\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4e2d\u6c92\u6709\u592a\u5927\u7684\u5f71\u97ff\uff0c\u7136\u800c\u8ddf\u672a\u52a0\u5165\u8072\u5b78\u7279\u5fb5\u8a13\u7df4\u7684\u5be6\u9a57\u6578\u64da\u76f8\u6bd4\u8f03\uff0c 0.319 \u878d\u5408\u5411\u91cf 0.543 0.481 0.533 0.392 0.266 \u82e5\u8981\u5be6\u8cea\u6027\u7684\u6539\u5584\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u7684\u7f3a\u9ede\uff0c\u4ecd\u9700\u66f4\u6df1\u5165\u7684\u63a2\u8a0e\u3002 \u6211\u5011\u767c\u73fe\u6578\u64da\u5176\u5be6\u5dee\u7570\u4e0d\u5927\uff0c\u6b64\u60c5\u6cc1\u53ef\u80fd\u662f\u56e0\u70ba\u6b64\u90e8\u5206\u7684\u5be6\u9a57\u53d7\u5230\u6ce8\u610f\u529b\u6a5f\u5236\u7684\u5f71\u97ff\u8f03 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L \u986f\u8457\uff0c\u8072\u5b78\u7279\u5fb5\u5c0d\u65bc\u6b64\u90e8\u5206\u5be6\u9a57\u4e0d\u662f\u5176\u8a13\u7df4\u7684\u91cd\u9ede\uff0c\u56e0\u6b64\u6c92\u6709\u986f\u8457\u7684\u63d0\u5347\u3002 \u627f\u4e0a\u6240\u8ff0\uff0c\u672a\u4f86\u7684\u7814\u7a76\u6211\u5011\u53ef\u4ee5\u91dd\u5c0d\u5e7e\u500b\u9762\u5411\u7e7c\u7e8c\u6df1\u5165\u3002\u9996\u5148\u662f\u61c9\u7528\u9810\u8a13\u7df4\u8a9e\u8a00\u6a21 0.380 \u878d\u5408\u5411\u91cf+\u5f37\u5316\u5b78\u7fd2 0.555 0.479 0.543 0.395 0.269 0.379 III. \u8072\u5b78\u7279\u5fb5+\u5f37\u5316\u5b78\u7fd2 \u7d93\u904e\u524d\u9762\u5169\u9805\u5be6\u9a57\u6bd4\u8f03\uff0c\u6211\u5011\u53ef\u4ee5\u767c\u73fe\u878d\u5408\u5411\u91cf\u53ef\u4ee5\u89e3\u6c7a\u90e8\u5206\u7684\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u5f71\u97ff\uff0c\u800c \u5f37\u5316\u5b78\u7fd2\u5247\u6bd4\u8f03\u5c08\u6ce8\u65bc\u6458\u8981\u8cc7\u8a0a\u6027\u3002\u56e0\u6b21\u6211\u5011\u5617\u8a66\u65bc\u6a21\u578b\u4e0a\u7d50\u5408\u8072\u5b78\u7279\u5fb5\u8207\u5f37\u5316\u5b78\u7fd2\u7684 \u65b9\u6cd5\uff0c\u5f9e\u8868 6 \u4e2d\uff0c\u6211\u5011\u53ef\u4ee5\u767c\u73fe\u5728\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u4e0a\uff0c\u6548\u679c\u6bd4\u8f03\u986f\u8457\u7684\u662f\u4f7f\u7528\u5c40\u90e8\u5411\u91cf\u7684 \u65b9\u5f0f\u7d50\u5408\u8072\u5b78\u7279\u5fb5\uff1b\u7136\u800c\u5728\u6587\u5b57\u6587\u4ef6\u6458\u8981\u4e2d\uff0c\u6bd4\u8f03\u597d\u7684\u7d50\u679c\u662f\u4f7f\u7528\u5168\u57df\u5411\u91cf\u3002\u56e0\u6b64\u6211\u5011 \u53ef\u4ee5\u63a8\u8ad6\u51fa\u8072\u5b78\u7279\u5fb5\u5c0d\u65bc\u4eba\u985e\u8f49\u5beb\u7684\u6587\u5b57\u6587\u4ef6\u6548\u7528\u4e0d\u5f70\uff0c\u800c\u5c0d\u65bc\u81ea\u52d5\u8fa8\u8b58\u7684\u8a9e\u97f3\u6587\u4ef6\u4e0a\uff0c \u9084\u662f\u6709\u4e0d\u932f\u7684\u6548\u679c\uff0c\u4f46\u53ef\u80fd\u9700\u8981\u8b93\u8072\u5b78\u7279\u5fb5\u76f4\u63a5\u53c3\u8207\u6458\u8981\u9078\u53d6\u7684\u968e\u6bb5\u624d\u80fd\u6709\u6548\u7684\u63d0\u5347\u6548 \u80fd\u3002\u7136\u800c\uff0c\u6574\u9ad4\u7684\u6578\u64da\u4e0a\u4ecd\u662f\u6bd4\u524d\u9762\u7684\u5be6\u9a57\u5dee\u4e86\u8a31\u591a\uff0c\u53ef\u80fd\u662f\u6a21\u578b\u4e0a\u9084\u9700\u4f5c\u66f4\u591a\u7d30\u90e8\u7684 \u8abf\u6574\uff0c\u6216\u7d50\u5408\u5176\u4ed6\u6a5f\u5236\u3002 \u8868 6. \u968e\u5c64\u5f0f\u985e\u795e\u7d93\u6458\u8981\u6a21\u578b-\u8072\u5b78\u7279\u5fb5+\u5f37\u5316\u5b78\u7fd2 [Table 6. Results of our model with acoustic features and reinforcement learning] \u6587\u5b57\u6587\u4ef6 \u8a9e\u97f3\u6587\u4ef6 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L Refresh [Narayan et al., 2018a] 0.453 0.372 0.446 0.329 0.197 0.319 \u7121\u8072\u5b78\u7279\u5fb5 0.479 0.400 0.469 0.352 0.226 0.342 \u5168\u57df\u5411\u91cf 0.486 0.400 0.473 0.350 0.222 0.336 \u5c40\u90e8\u5411\u91cf 0.478 0.399 0.469 0.384 0.264 0.370 \u5168\u57df\u5411\u91cf+\u5c40\u90e8\u5411\u91cf 0.464 0.373 0.453 0.350 0.224 0.336 Refresh 0.453 0.372 0.446 0.329 0.197 0.319 \u8868 9. \u968e\u5c64\u5f0f\u985e\u795e\u7d93\u6458\u8981\u6a21\u578b-\u7d9c\u5408\u6bd4\u8f03 \u578b\u65bc\u6458\u8981\u7814\u7a76\u4e0a\uff0c\u6539\u5584\u8a9e\u53e5\u6216\u6587\u7ae0\u7684\u8a9e\u610f\u8868\u793a\uff0c\u7531\u65bc\u6700\u8fd1\u6709\u8a31\u591a\u9810\u8a13\u7df4\u8a9e\u8a00\u6a21\u578b\u5df2\u7d93\u4f7f [Narayan et al., 2018a] \u8a5e\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236 0.523 0.472 0.519 \u7528\u76f8\u7576\u5927\u91cf\u7684\u8cc7\u6599\u53ca\u9ad8\u6548\u80fd\u7684\u8a2d\u5099\u9032\u884c\u8a13\u7df4\uff0c\u4e14\u5df2\u88ab\u8b49\u660e\u5728\u8a31\u591a\u4efb\u52d9\u4e0a\u6709\u76f8\u7576\u4eae\u773c\u7684\u6210 [Table 9. Comprehensive comparison of our models] 0.401 0.290 0.392 \u5b57\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236 0.535 0.477 0.529 0.368 0.245 \u6587\u5b57\u6587\u4ef6 \u7e3e\uff0c\u50c5\u9700\u91dd\u5c0d\u61c9\u7528\u5fae\u8abf\u5373\u53ef\uff0c\u6216\u8a31\u53ef\u4ee5\u5617\u8a66\u9032\u884c\u6df1\u5165\u7814\u7a76\uff1b\u5176\u6b21\u662f\u91cd\u65b0\u6574\u7406\u8cc7\u6599\u96c6\uff0c\u56e0 \u8a9e\u97f3\u6587\u4ef6 \u70ba\u6458\u8981\u9023\u8cab\u6027\u5c0d\u65bc\u6458\u8981\u4ea6\u662f\u76f8\u7576\u91cd\u8981\u7684\u6307\u6a19\uff0c\u82e5\u6210\u672c\u5141\u8a31\uff0c\u5247\u53ef\u4ee5\u50f1\u8acb\u5c08\u5bb6\u5e6b\u5fd9\u70ba\u8cc7\u6599 0.356 \u878d\u5408\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236 0.567 0.496 0.557 0.402 0.278 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L \u9032\u884c\u91cd\u65b0\u6a19\u8a3b\uff0c\u9664\u4e86\u6a19\u8a3b\u6458\u8981\u8a9e\u53e5\u5916\uff0c\u540c\u6642\u4ea6\u52a0\u5165\u6458\u8981\u8a9e\u53e5\u7684\u9806\u5e8f\uff0c\u66f4\u6709\u5229\u65bc\u5f8c\u7e8c\u7684\u6458 0.389 V. \u6b21\u8a5e\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236+\u5f37\u5316\u5b78\u7fd2 Refresh 0.453 0.372 0.446 0.329 0.197 0.319 \u8981\u6392\u5e8f\u76f8\u95dc\u7814\u7a76\uff1b\u518d\u8005\uff0c\u7bc0\u9304\u5f0f\u6458\u8981\u4ea6\u53ef\u80fd\u767c\u751f\u8a9e\u53e5\u9593\u8a9e\u610f\u91cd\u8907\u7684\u60c5\u6cc1\uff0c\u7136\u800c\u9bae\u5c11\u5b78\u8005 [Narayan et al., 2018a] \u91dd\u5c0d\u7bc0\u9304\u5f0f\u6458\u8981\u91cd\u8907\u6027\u9032\u884c\u7814\u7a76\uff0c\u56e0\u6b64\u70ba\u4e86\u6e1b\u5c11\u7bc0\u9304\u5f0f\u6458\u8981\u4e4b\u91cd\u8907\u6027\uff0c\u6216\u8a31\u53ef\u5c07\u91cd\u5beb\u5f0f \u63a5\u7e8c\u524d\u4e00\u500b\u5be6\u9a57\uff0c\u6211\u5011\u52a0\u5165\u5f37\u5316\u5b78\u7fd2\u6a5f\u5236\u65bc\u8a13\u7df4\u4e2d\uff0c\u5be6\u9a57\u7d50\u679c\u5982\u8868 8 \u6240\u793a\u3002\u5f9e\u7d50\u679c\u53ef\u4ee5 \u767c\u73fe\uff0c\u4e0d\u7ba1\u662f\u6587\u5b57\u6587\u4ef6\u9084\u662f\u8a9e\u97f3\u6587\u4ef6\uff0c\u52a0\u5165\u5f37\u5316\u5b78\u7fd2\u6a5f\u5236\u5f8c\uff0c\u7686\u662f\u5728\u8f38\u5165\u70ba\u8a5e\u5411\u91cf\u6642\u6703 \u878d\u5408\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236 +\u5f37\u5316\u5b78\u7fd2 0.518 0.448 0.502 0.347 0.209 \u6458\u8981\u7814\u7a76\u4e2d\u5e38\u898b\u4e4b\u6e1b\u5c11\u5197\u4f59\u7684\u6a5f\u5236\u6539\u826f\u4e26\u61c9\u7528\u65bc\u6211\u5011\u7684\u65b9\u6cd5\u4e0a\uff0c\u61c9\u80fd\u5f97\u5230\u66f4\u5177\u610f\u7fa9\u7684\u6458 0.337 \u8981\u7d50\u679c\uff1b\u6700\u5f8c\u4e5f\u6700\u91cd\u8981\u7684\u662f\u9700\u8981\u907f\u514d\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u5f71\u97ff\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u6548\u679c\uff0c\u5f9e\u6211\u5011\u7684\u5be6 \u5f97\u5230\u8f03\u597d\u7684\u6548\u679c\u3002\u9019\u6709\u53ef\u80fd\u662f\u56e0\u70ba\u6211\u5011\u7684\u5f37\u5316\u5b78\u7fd2\u4e2d\u734e\u52f5\u51fd\u6578\u4f7f\u7528 ROUGE \u5206\u6578\uff0c\u800c \u878d\u5408\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236 0.567 0.496 0.557 0.402 0.278 0.389 \u9a57\u53ef\u4ee5\u5f97\u51fa\uff0c\u73fe\u4eca\u7684\u65b9\u6cd5\u4ecd\u6709\u6240\u4fb7\u9650\uff0c\u800c\u70ba\u4e86\u6709\u6548\u5730\u63d0\u5347\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u6e96\u78ba\u6027\uff0c\u6216\u8a31\u6211 ROUGE \u8a08\u7b97\u6642\u4e3b\u8981\u662f\u4ee5\u8a5e\u70ba\u57fa\u672c\u55ae\u4f4d\uff0c\u56e0\u800c\u5c0e\u81f4\u5728\u5176\u4ed6\u60c5\u6cc1\u4e0b\u7d50\u679c\u76f8\u5c0d\u8f03\u5dee\u3002 \u8868 8. \u968e\u5c64\u5f0f\u985e\u795e\u7d93\u6458\u8981\u6a21\u578b-\u6b21\u8a5e\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236+\u5f37\u5316\u5b78\u7fd2 [Table 8. Results of our model with sub-word information, attention mechanism and reinforcement learning] \u6587\u5b57\u6587\u4ef6 \u8a9e\u97f3\u6587\u4ef6 \u8a5e\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236+ \u76f8\u95dc\u7814\u7a76\u7684\u51fa\u73fe\u3002\u96d6\u7136\u591a\u5a92\u9ad4\u6280\u8853\u9032\u6b65\u5feb\u901f\uff0c\u4f46\u5927\u591a\u6578\u7684\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u65b9\u6cd5\u4ecd\u591a\u534a\u7531\u6587 \u5f37\u5316\u5b78\u7fd2 0.525 0.451 0.515 0.342 0.221 0.329 \u4efb\u52d9\u4e0a\u771f\u7684\u6709\u4e00\u5b9a\u7684\u6210\u6548\u3002 \u5b78\u7fd2\u6280\u8853\u7684\u84ec\u52c3\u767c\u5c55\uff0c\u4f7f\u5f97\u591a\u5a92\u9ad4\u6587\u4ef6\u76f8\u95dc\u7814\u7a76\u66f4\u70ba\u5bb9\u6613\uff0c\u56e0\u800c\u9010\u6f38\u6709\u591a\u5a92\u9ad4\u6587\u4ef6\u6458\u8981 \u5b57\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236+ \u904e\u53bb\u6709\u95dc\u81ea\u52d5\u6587\u4ef6\u6458\u8981\u7684\u7814\u7a76\u4e3b\u8981\u4ecd\u8457\u91cd\u65bc\u6587\u5b57\u6587\u4ef6\u6458\u8981\uff1b\u800c\u8fd1\u5e74\u4f86\u7531\u65bc\u5927\u6578\u64da\u53ca\u6a5f\u5668 \u5011\u7684\u6458\u8981\u7cfb\u7d71\u9078\u51fa\u7684\u6458\u8981\u5927\u90e8\u5206\u548c\u53c3\u8003\u6458\u8981\u76f8\u540c\uff0c\u56e0\u6b64\u53ef\u9a57\u8b49\u6211\u5011\u7684\u6ce8\u610f\u529b\u6a5f\u5236\u65bc\u6458\u8981 \u5f37\u5316\u5b78\u7fd2 0.543 0.491 0.539 0.350 0.226 0.337 \u88ab\u8996\u70ba\u6458\u8981\uff0c\u5176\u4e2d\u88ab\u7d05\u6846\u5708\u8d77\u7684\u5217\u70ba\u53c3\u8003\u6458\u8981\u3002\u5f9e\u7d05\u6846\u7684\u90e8\u5206\u770b\u53ef\u4ee5\u5f88\u660e\u986f\u7684\u767c\u73fe\uff0c\u6211 5. \u7d50\u8ad6\u8207\u672a\u4f86\u5c55\u671b (Conclusion & Future Work) \u6458\u8981\u7684\u6a5f\u7387\u3002\u82e5\u8a72\u5217\u4e2d\u6bcf\u6b04\u7684\u984f\u8272\u8d8a\u6df1\uff0c\u5247\u4ee3\u8868\u8a72\u53e5\u548c\u5176\u4ed6\u53e5\u7684\u95dc\u806f\u6027\u8d8a\u5927\uff0c\u5247\u8a72\u53e5\u4e5f [Narayan et al., 2018a] 0.453 0.372 0.446 0.329 0.197 0.319 \u4ef6\u4e2d\u7684\u8a9e\u53e5\uff0c\u6bcf\u500b\u5217\u7684\u8a9e\u53e5\u6a19\u865f\u65c1\u62ec\u5f27\u5167\u7684\u6578\u503c\u70ba 1| , , \uff0c\u5373\u8a72\u53e5\u88ab\u8fa8\u8b58\u70ba \u6216\u8a31\u66f4\u7b26\u5408\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\uff0c\u4ea6\u80fd\u6709\u8f03\u512a\u7570\u7684\u6210\u6548\u3002 Refresh \u6587\u4ef6\u6458\u8981\u7684\u6210\u6548\uff0c\u6211\u5011\u8a8d\u70ba\u4ecd\u9808\u5f9e\u8a9e\u97f3\u8fa8\u8b58\u7684\u90e8\u5206\u8457\u624b\uff0c\u82e5\u80fd\u4e0d\u7d93\u904e\u8f49\u5beb\u76f4\u63a5\u64f7\u53d6\u6458\u8981\uff0c \u53e6\u5916\uff0c\u6211\u5011\u4ea6\u91dd\u5c0d\u6ce8\u610f\u529b\u6a5f\u5236\u4e2d\u7684\u6b0a\u91cd\u9032\u884c\u5206\u6790(\u5716 7)\uff0c\u5716\u4e2d\u6bcf\u500b\u5217\u548c\u884c\u4ee3\u8868\u4ee3\u8868\u6587 ROUGE-1 ROUGE-2 ROUGE-L ROUGE-1 ROUGE-2 ROUGE-L \u878d\u5408\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236 +\u8072\u5b78\u7279\u5fb5+\u5f37\u5316\u5b78\u7fd2 0.532 0.455 0.521 0.336 0.220 0.326 \u5011\u80fd\u5617\u8a66\u4f7f\u7528\u8a9e\u97f3\u7279\u5fb5\u5982 Fbank \u548c MFCC \u7b49\u4f5c\u70ba\u6458\u8981\u7cfb\u7d71\u4e4b\u8f38\u5165\uff0c\u61c9\u53ef\u5f97\u5230\u8f03\u539f\u59cb\u7684\u8a9e \u5716 7. \u6ce8\u610f\u529b\u6a5f\u5236\u6b0a\u91cd\u8996\u89ba\u5316 \u97f3\u5167\u5bb9\uff0c\u4ea6\u80fd\u6e1b\u5c11\u9047\u5230\u8fa8\u8b58\u932f\u8aa4\u7684\u60c5\u6cc1\uff0c\u4e14\u56e0\u7bc0\u9304\u5f0f\u6458\u8981\u662f\u9032\u884c\u8a9e\u53e5\u9078\u64c7\uff0c\u56e0\u6b64\u4e0d\u9700\u518d [Figure 7. Visualization of attention weight] \u9032\u884c\u8f49\u5beb\uff0c\u56e0\u800c\u80fd\u4f7f\u5f97\u6458\u8981\u540c\u70ba\u8a9e\u97f3\u5f62\u5f0f\uff0c\u4f46\u6b64\u60f3\u6cd5\u9700\u8981\u591a\u52a0\u8003\u616e\u7684\u90e8\u5206\u5728\u65bc\u96e3\u4ee5\u8a55\u4f30 \u878d\u5408\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236 0.569 0.507 0.561 0.401 0.288 0.394 \u7c21\u55ae\u7e3d\u7d50\u6574\u9ad4\u5be6\u9a57\u7d50\u679c\uff0c\u6211\u5011\u63d0\u51fa\u4e4b\u6a21\u578b\u67b6\u69cb\u78ba\u5be6\u53ef\u6709\u6548\u63d0\u5347\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u7684\u6210\u6548\uff0c \u7d50\u679c\u6b63\u78ba\u8207\u5426\uff0c\u4e5f\u76f8\u8f03\u5169\u968e\u6bb5\u7684\u65b9\u6cd5\u96e3\u5be6\u73fe\uff0c\u56e0\u6b64\u8f03\u5c11\u5b78\u8005\u6295\u5165\u9019\u65b9\u9762\u7684\u7814\u7a76\uff0c\u82e5\u80fd\u5be6 +\u8072\u5b78\u7279\u5fb5 \u529b\u6a5f\u5236\u548c\u5f37\u5316\u5b78\u7fd2\u7b49\u65b9\u6cd5\u5c0d\u65bc\u6587\u5b57\u6587\u4ef6\u7684\u6548\u679c\u4ecd\u6bd4\u8f03\u986f\u8457\u3002\u56e0\u6b64\u82e5\u8981\u5be6\u8cea\u6027\u5730\u63d0\u5347\u8a9e\u97f3 VII. \u8996\u89ba\u5316\u6ce8\u610f\u529b \u7136\u800c\u5c0d\u65bc\u907f\u514d\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u7684\u5f71\u97ff\u4e0a\uff0c\u6b21\u8a5e\u5411\u91cf\u548c\u8072\u5b78\u7279\u5fb5\u7684\u6548\u679c\u4ecd\u6709\u5f85\u52a0\u5f37\uff1b\u800c\u6ce8\u610f \u73fe\u6211\u5011\u7684\u69cb\u60f3\uff0c\u61c9\u53ef\u4f7f\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u6280\u8853\u9054\u5230\u65b0\u7684\u9ad8\u5ea6\uff0c\u4ea6\u9020\u798f\u65e5\u5f8c\u7684\u5b78\u8005\u5011\u3002</td></tr><tr><td colspan=\"2\">\u7b49\u3002\u6b64\u8cc7\u6599\u96c6\u5176\u4e2d\u6709 205 \u7bc7\u5ee3\u64ad\u65b0 \u805e\u6587\u4ef6\u9069\u7528\u65bc\u6458\u8981\u5be6\u9a57\uff0c\u6211\u5011\u6311\u9078\u5176\u4e2d\u7684 20 \u7bc7\u4f5c\u70ba\u6e2c\u8a66\u96c6\uff0c\u9918\u4e0b\u7684 185 \u7bc7\u5247\u70ba\u8a13\u7df4\u96c6\u3002 \u5171\u632f\u5cf0\u662f\u983b\u8b5c\u4e2d\u7684\u5cf0\u503c\uff0c\u4e3b\u8981\u7528\u4f86\u63cf\u8ff0\u4eba\u985e\u8072\u9053\u5167\u7684\u5171\u632f\u60c5\u5f62\u3002\u5982\u679c\u8072\u97f3\u6bd4\u8f03\u4f4e\u6c88\uff0c \u5247\u5171\u632f\u5cf0\u6703\u6bd4\u8f03\u660e\u986f\uff0c\u807d\u5230\u7684\u5167\u5bb9\u4ea6\u6703\u8f03\u6e05\u6670\uff1b\u53cd\u4e4b\u82e5\u8072\u97f3\u592a\u904e\u9ad8\u4ea2\uff0c\u5247\u5171\u632f\u92d2\u6703 \u6bd4\u8f03\u6a21\u7cca\uff0c\u540c\u6642\u807d\u5230\u7684\u5167\u5bb9\u4e5f\u6703\u6bd4\u8f03\u6a21\u7cca\u96e3\u8fa8\u3002 Refresh [Narayan et al., 2018a] 0.453 0.372 0.446 0.329 0.197 0.319 \u9996\u5148\u6211\u5011\u53ef\u4ee5\u5f9e\u8868\u4e2d\u767c\u73fe\u50b3\u7d71\u7684\u5411\u91cf\u7a7a\u9593\u6a21\u578b(Vector space model, VSM)\u5728\u6587\u5b57\u6587\u4ef6 \u548c\u8a9e\u97f3\u6587\u4ef6\u4e0a\u7684\u6548\u679c\u6c92\u6709\u5dee\u7570\u592a\u5927\uff0c\u4f46\u6587\u5b57\u6587\u4ef6\u7684\u6548\u679c\u4ecd\u662f\u6bd4\u8a9e\u97f3\u6587\u4ef6\u512a\u7570\uff1b\u53e6\u5916\u6211\u5011 \u53ef\u4ee5\u5c07 VSM \u8ddf LSA \u4f5c\u4e00\u500b\u7c21\u55ae\u7684\u6bd4\u8f03\uff0c\u53ef\u4ee5\u767c\u73fe LSA \u7684\u7d50\u679c\u80fd\u5f88\u660e\u986f\u7684\u770b\u51fa\u6587\u5b57\u6587\u4ef6 \u548c\u8a9e\u97f3\u6587\u4ef6\u7684\u5dee\u7570\uff0c\u540c\u6642\u4e5f\u6bd4 VSM \u7684\u6548\u679c\u597d\u8a31\u591a\u3002 \u63a5\u8457\u6211\u5011\u5f9e\u975e\u76e3\u7763\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u67b6\u69cb\u7684\u7d50\u679c\u89c0\u5bdf\uff0cSG(Skip-gram)\u548c CBOW \u61c9\u7528\u65bc \u878d\u5408\u5411\u91cf(\u8a5e+\u5b57) 0.543 0.481 0.533 0.392 0.266 0.380 II. \u5f37\u5316\u5b78\u7fd2 \u627f\u4e0a\u6240\u8ff0\uff0c\u6211\u5011\u8a8d\u70ba\u878d\u5408\u5411\u91cf\u65bc\u8a9e\u97f3\u6458\u8981\u4e0a\u6709\u76f8\u7576\u5927\u7684\u53ef\u80fd\u6027\uff0c\u56e0\u6b64\u6211\u5011\u5617\u8a66\u540c\u6642\u4f7f\u7528 \u878d\u5408\u5411\u91cf\u548c\u5f37\u5316\u5b78\u7fd2\u65bc\u6a21\u578b\u4e0a\uff0c\u5f9e\u8868 5 \u4e2d\u53ef\u4ee5\u5f88\u660e\u986f\u7684\u770b\u5230\u5f37\u5316\u5b78\u7fd2\u65bc\u6211\u5011\u7684\u65b9\u6cd5\u4e2d\u6709 \u4e00\u5b9a\u7684\u6210\u6548\u5728\uff0c\u4e0d\u904e\u5728\u6587\u5b57\u6587\u4ef6\u6458\u8981\u4e0a\u6709\u6bd4\u8f03\u591a\u7684\u9032\u6b65\uff0c\u4e3b\u56e0\u53ef\u80fd\u662f\u5728\u65bc\u53c3\u8003\u6458\u8981\u4e0d\u5305 \u542b\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\uff0c\u56e0\u6b64\u6c92\u6709\u8fa6\u6cd5\u5b8c\u5168\u89e3\u6c7a\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u7684\u5f71\u97ff\uff0c\u82e5\u80fd\u5c07\u8072\u5b78\u7279\u5fb5\u4ea6\u52a0\u5165 \u5f37\u5316\u5b78\u7fd2\u7684\u734e\u52f5\u51fd\u6578\u4e2d\u6216\u8a31\u80fd\u6539\u9032\u6b64\u60c5\u6cc1\u3002 \u9078\u64c7\u5411\u91cf 0.448 0.371 0.439 0.350 0.213 0.334 \u878d\u5408\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236 0.518 0.448 0.502 0.347 0.209 0.337 \u5b57\u6587\u4ef6\u6458\u8981\u65b9\u6cd5\u5ef6\u4f38\u800c\u4f86\u3002\u76f4\u81f3\u8fd1\u671f\u96a8\u8457\u6df1\u5c64\u5b78\u7fd2\u6280\u8853\u6f38\u8da8\u6210\u719f\uff0c\u591a\u5a92\u9ad4\u6587\u4ef6\u6458\u8981\u6280\u8853 +\u5f37\u5316\u5b78\u7fd2 \u4e5f\u96a8\u4e4b\u6210\u9577\u3002 IV. \u6b21\u8a5e\u5411\u91cf+\u6ce8\u610f\u529b\u6a5f\u5236 VI. \u7d9c\u5408\u6bd4\u8f03 \u9806\u61c9\u6df1\u5c64\u5b78\u7fd2\u7684\u6d6a\u6f6e\uff0c\u672c\u8ad6\u6587\u63d0\u51fa\u4e00\u7a2e\u968e\u5c64\u5f0f\u985e\u795e\u7d93\u7db2\u8def\u67b6\u69cb\u4f86\u5f9e\u4e8b\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\uff0c \u56e0\u524d\u4e00\u500b\u5be6\u9a57\u7d50\u679c\u767c\u73fe\u8072\u5b78\u7279\u5fb5\u548c\u5f37\u5316\u5b78\u7fd2\u5171\u540c\u8a13\u7df4\u6642\u6548\u679c\u76f8\u5c0d\u8f03\u5dee\uff0c\u56e0\u6b64\u6211\u5011\u9019\u6b21\u6bd4 \u6700\u5f8c\uff0c\u6211\u5011\u5c07\u524d\u8ff0\u63d0\u5230\u4e4b\u67b6\u69cb\u505a\u4e00\u500b\u7d9c\u5408\u6bd4\u8f03\uff0c\u5be6\u9a57\u7d50\u679c\u5982\u8868 9 \u6240\u793a\u3002\u5176\u4e2d\u6211\u5011\u53ef\u4ee5\u767c \u540c\u6642\u4ea6\u9069\u7528\u65bc\u4e00\u822c\u6587\u5b57\u6587\u4ef6\u6458\u8981\u3002\u6587\u4ef6\u6458\u8981\u4efb\u52d9\u53ef\u6982\u5206\u70ba\u7bc0\u9304\u5f0f\u8207\u91cd\u5beb\u5f0f\u6458\u8981\u3002\u672c\u8ad6\u6587 \u8f03\u7d50\u5408\u6b21\u8a5e\u5411\u91cf\u548c\u6ce8\u610f\u529b\u6a5f\u5236\u7684\u5be6\u9a57\u7d50\u679c\u3002\u5f9e\u8868 7 \u4e2d\u53ef\u4ee5\u767c\u73fe\u540c\u6642\u4f7f\u7528\u878d\u5408\u5411\u91cf\u548c\u6ce8\u610f \u73fe\u7576\u5f37\u5316\u5b78\u7fd2\u6a5f\u5236\u548c\u6ce8\u610f\u529b\u6a5f\u5236\u540c\u6642\u4f7f\u7528\u7684\u60c5\u6cc1\u4e0b\uff0c\u4e0d\u7ba1\u662f\u5728\u6587\u5b57\u6587\u4ef6\u9084\u662f\u8a9e\u97f3\u6587\u4ef6\u4e0a \u65e8\u5728\u63a2\u8a0e\u7bc0\u9304\u5f0f\u8a9e\u97f3\u6587\u4ef6\u6458\u8981\u65b9\u6cd5\u3002\u5176\u4e2d\u70ba\u4e86\u63d0\u5347\u6458\u8981\u8cc7\u8a0a\u6027\u53ca\u9023\u8cab\u6027\uff0c\u6211\u5011\u52a0\u5165\u6ce8\u610f \u529b\u6a5f\u5236\u7684\u6548\u679c\u5728\u6587\u5b57\u6587\u4ef6\u4e0a\u8f03\u70ba\u512a\u7570\uff0c\u800c\u5728\u8a9e\u97f3\u6587\u4ef6\u4e0a\u4ecd\u662f\u4ee5\u8a5e\u5411\u91cf\u7684\u7d50\u679c\u6bd4\u8f03\u597d\u3002\u96d6 \u6548\u679c\u90fd\u76f8\u5c0d\u8f03\u5dee\u3002\u6b64\u7a2e\u60c5\u6cc1\u6709\u53ef\u80fd\u662f\u56e0\u70ba\u6211\u5011\u7684\u6ce8\u610f\u529b\u6a5f\u5236\u4e3b\u8981\u91dd\u5c0d\u7684\u662f\u6458\u8981\u8cc7\u8a0a\u6027\u63d0 \u529b\u6a5f\u5236\u53ca\u5f37\u5316\u5b78\u7fd2\u6280\u8853\uff1b\u53e6\u5916\u6211\u5011\u4ea6\u5617\u8a66\u4f7f\u7528\u8072\u5b78\u7279\u5fb5\u53ca\u6b21\u8a5e\u5411\u91cf\u65bc\u6a21\u578b\u8a13\u7df4\u4e2d\uff0c\u4ee5\u907f \u7136\u6574\u9ad4\u7684\u6548\u679c\u7686\u6bd4\u4e4b\u524d\u7684\u7d50\u679c\u597d\uff0c\u4f46\u53ef\u80fd\u662f\u56e0\u70ba\u6ce8\u610f\u529b\u6a5f\u5236\u8a13\u7df4\u7684\u4e3b\u8981\u662f\u6587\u4ef6\u4e2d\u8a9e\u53e5\u4e4b \u5347\uff0c\u800c\u5f37\u5316\u5b78\u7fd2\u4e2d\u7531\u65bc\u4f7f\u7528 ROUGE \u5206\u6578\u4f5c\u70ba\u734e\u52f5\u51fd\u6578\uff0c\u800c ROUGE \u4e5f\u662f\u8a08\u7b97\u6458\u8981\u8cc7\u8a0a \u514d\u8a08\u7b97\u6458\u8981\u6642\u53d7\u5230\u904e\u591a\u8a9e\u97f3\u8fa8\u8b58\u932f\u8aa4\u5f71\u97ff\u3002\u7d93\u7531\u4e00\u7cfb\u5217\u7684\u5be6\u9a57\u5206\u6790\u8207\u8a0e\u8ad6\uff0c\u9996\u5148\u6211\u5011\u767c</td></tr></table>", | |
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