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Register a new userSUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents
STARM: مجموعة بيانات لاستخراج الموجز الذاتي من وثائق ويكيبيديا
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تتطلب العديد من التطبيقات توليد ملخصات مصممة خصيصا لاحتياجات معلومات المستخدم، أي نواياها. الأساليب التي تعبر عن النية عبر استعلامات المستخدم الصريحة تسقط قصيرة عند التفسير الاستعلام هو شخصي. توجد عدة مجموعات من مجموعات البيانات للتخصيص مع النوايا الموضوعية حيث، لكل وثيقة ونوايا (E.G.، Weather ")، تكفي موجز واحد لجميع المستخدمين. لا توجد مجموعات البيانات، ومع ذلك، بالنسبة للمؤلفة الذاتية (E.G.، الأماكن المثيرة للاهتمام ") حيث سيقدم المستخدمون المختلفون ملخصات مختلفة. نحن نقدم العانة، أول مجموعة البيانات لتقييم أنظمة استخراج الملخص الذاتي. تحتوي STALUME على ثلاثة أفراد (وثيقة، نية، ملخص) ثلاثة توائم أكثر من 48 صفحة ويكيبيديا، مع عشرة نوبة ذاتي اختلاف ذاتي، والتي توفرها 103 فردا على الترك الميكانيكي. نوضح إحصائيا أن النوايا في SARMENT تختلف بشكل منهجي في الذاتية. للإشارة إلى فائدة SUTTUME، نستكشف مجموعة من خوارزميات أساسية لتلخيص استخراجي ذاتي وإظهار أن (I) كما هو متوقع، فإن النهج القائمة على سبيل المثال، من الأفضل أن تلتقط النوايا ذاتية من تلك القائمة على الاستعلام، و (2) هناك نطاق واسع لتحسينه خوارزميات الأساس، وبالتالي تحفز المزيد من الأبحاث حول هذه المشكلة الصعبة.
Many applications require generation of summaries tailored to the user's information needs, i.e., their intent. Methods that express intent via explicit user queries fall short when query interpretation is subjective. Several datasets exist for summarization with objective intents where, for each document and intent (e.g., weather''), a single summary suffices for all users. No datasets exist, however, for subjective intents (e.g., interesting places'') where different users will provide different summaries. We present SUBSUME, the first dataset for evaluation of SUBjective SUMmary Extraction systems. SUBSUME contains 2,200 (document, intent, summary) triplets over 48 Wikipedia pages, with ten intents of varying subjectivity, provided by 103 individuals over Mechanical Turk. We demonstrate statistically that the intents in SUBSUME vary systematically in subjectivity. To indicate SUBSUME's usefulness, we explore a collection of baseline algorithms for subjective extractive summarization and show that (i) as expected, example-based approaches better capture subjective intents than query-based ones, and (ii) there is ample scope for improving upon the baseline algorithms, thereby motivating further research on this challenging problem.
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