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Register a new userExtractive Opinion Summarization in Quantized Transformer Spaces
تلخيص الرأي الاستخراجي في مساحات محول كمية
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الملخص نقدم المحول الكمي (كيو تي)، نظام غير مؤظفي لتلخيص الرأي الاستخراجي.يستلهم كيو تي عن طريق السيارات الآلية المتناقلة الكمية، والتي نعدها لتلخيص الشعبية.يستخدم تفسير تجميع الفضاء الكمي وقواريل استخراج جديدة لاكتشاف الآراء الشعبية بين مئات من المراجعات، وهي خطوة كبيرة نحو تلخيص الرأي للنطاق العملي.بالإضافة إلى ذلك، تتيح كيو تي تلخيص قابل للتحكم دون مزيد من التدريب، من خلال الاستفادة من خصائص المساحة الكمي لاستخراج الملخصات الخاصة بالجانب.كما نجعل مساحة متاحة للجمهور، معيار تقييم واسع النطاق لرموز الرأي، يشتمل على ملخصات عامة وجوقية خاصة ب 50 فندقا.توضح التجارب وعد نهجنا، والتي تتم التحقق من صحتها عن طريق الدراسات الإنسانية حيث أظهر القضاة تفضيل واضح لطريقنا على خطوط الأساس التنافسية.
Abstract We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization. QT is inspired by Vector- Quantized Variational Autoencoders, which we repurpose for popularity-driven summarization. It uses a clustering interpretation of the quantized space and a novel extraction algorithm to discover popular opinions among hundreds of reviews, a significant step towards opinion summarization of practical scope. In addition, QT enables controllable summarization without further training, by utilizing properties of the quantized space to extract aspect-specific summaries. We also make publicly available Space, a large-scale evaluation benchmark for opinion summarizers, comprising general and aspect-specific summaries for 50 hotels. Experiments demonstrate the promise of our approach, which is validated by human studies where judges showed clear preference for our method over competitive baselines.
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The amount of information available online can be overwhelming for users to digest, specially when dealing with other users' comments when making a decision about buying a product or service. In this context, opinion summarization systems are of grea
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