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Register a new userWikiAsp: A Dataset for Multi-domain Aspect-based Summarization
WikiAiAIASP: مجموعة بيانات لتلخيص القائم على الجانب المجال
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التلخصات القائمة على الجانب المجردة هي مهمة توليد ملخصات مركزة تستند إلى نقاط اهتمام محددة. هذه الملخصات تساعد تحليل فعال للنص، مثل فهم الاستعراضات أو الآراء بسرعة من زوايا مختلفة. ومع ذلك، نظرا للاختلافات الكبيرة في نوع الجوانب لمجالات مختلفة (مثل المشاعر، ميزات المنتج)، تميل تطوير النماذج السابقة إلى أن تكون خاصة بالمجال. في هذه الورقة، نقترح WikiAsp، 1 مجموعة بيانات واسعة النطاق لتلخيص القائم على الجانب متعدد المجالات التي تحاول تحفيز البحث في اتجاه التلخيص المستند إلى جانب النطاق. على وجه التحديد، نبني DataSet باستخدام مقالات Wikipedia من 20 مجالات مختلفة، باستخدام عناوين القسم وحدود كل مقال كوكيل للتعليق على الجانب. نقترح العديد من النماذج الأساسية المباشرة لهذه المهمة وإجراء تجارب على مجموعة البيانات. تسليط الضوء على النتائج التحديات الرئيسية التي تواجهها نماذج التلخيص الموجودة في هذا الإعداد، مثل التعامل مع الضمير المناسب للمصادر المعروضة والشرح المستمر للأحداث الحساسة للوقت.
Abstract Aspect-based summarization is the task of generating focused summaries based on specific points of interest. Such summaries aid efficient analysis of text, such as quickly understanding reviews or opinions from different angles. However, due to large differences in the type of aspects for different domains (e.g., sentiment, product features), the development of previous models has tended to be domain-specific. In this paper, we propose WikiAsp,1 a large-scale dataset for multi-domain aspect- based summarization that attempts to spur research in the direction of open-domain aspect-based summarization. Specifically, we build the dataset using Wikipedia articles from 20 different domains, using the section titles and boundaries of each article as a proxy for aspect annotation. We propose several straightforward baseline models for this task and conduct experiments on the dataset. Results highlight key challenges that existing summarization models face in this setting, such as proper pronoun handling of quoted sources and consistent explanation of time-sensitive events.
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