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Register a new userKeyphrase Extraction from Scientific Articles via Extractive Summarization
استخراج المفاتيح من المقالات العلمية عبر تلخيص الاستخراج
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يؤدي استخراج الموجات القصيرة تلقائيا من المستندات العلمية إلى تمثيل موجز قيم يمكن أن يفهم البشر ويمكنهم معالجة الآلات للمهام، مثل استرجاع المعلومات، تجميع المقالات وتصنيف المادة.تهتم هذه الورقة بأجزاء مقالة علمية يجب أن تعطى كمدخلات لطرق استخراج الحرارة.تأخذ أساليب التعلم العميق الحديثة الألقاب والملخصات كمدخلات بسبب زيادة التعقيد الحسابي في معالجة التسلسلات الطويلة، في حين أن الأساليب التقليدية يمكن أن تعمل أيضا مع النصوص الكاملة.العناوين والملخصات كثيفة في الجماهيرية، ولكن غالبا ما تفوت جوانب مهمة من المقالات، في حين أن النصوص الكاملة من ناحية أخرى أكثر ثراء في الجماهيرية ولكن الكثير من الضويرة.لمعالجة هذه المفاضلة، نقترح استخدام نماذج تلخيص الاستخراجية على النصوص الكاملة للمستندات العلمية.تظهر دراستنا التجريبية على 3 مجموعات مقالات باستخدام 3 طرق استخراج المفاتيح 3 نتائج واعدة.
Automatically extracting keyphrases from scholarly documents leads to a valuable concise representation that humans can understand and machines can process for tasks, such as information retrieval, article clustering and article classification. This paper is concerned with the parts of a scientific article that should be given as input to keyphrase extraction methods. Recent deep learning methods take titles and abstracts as input due to the increased computational complexity in processing long sequences, whereas traditional approaches can also work with full-texts. Titles and abstracts are dense in keyphrases, but often miss important aspects of the articles, while full-texts on the other hand are richer in keyphrases but much noisier. To address this trade-off, we propose the use of extractive summarization models on the full-texts of scholarly documents. Our empirical study on 3 article collections using 3 keyphrase extraction methods shows promising results.
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Automatic summarization aims to extract important information from large amounts of textual data in order to create a shorter version of the original texts while preserving its information. Training traditional extractive summarization models relies
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Keyword or keyphrase extraction is to identify words or phrases presenting the main topics of a document. This paper proposes the AttentionRank, a hybrid attention model, to identify keyphrases from a document in an unsupervised manner. AttentionRank
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