في سيناريو دردشة خدمة العملاء النموذجي، اتصل العملاء بمركز دعم لطلب المساعدة أو رفع الشكاوى، وحاول الوكلاء البشريون حل المشكلات.في معظم الحالات، يطلب من الوكلاء في نهاية المحادثة كتابة ملخص قصير يؤكد على المشكلة والحل المقترح، عادة من أجل فائدة الوكلاء الآخرين الذين قد يتعين عليهم التعامل مع نفس العميل أو المشكلة.الهدف من هذه المقالة يدعى إلى أتمتة هذه المهمة.نقدم مجموعة بيانات ملخصات حوار عملاء عالية الجودة وعالية الجودة ذات الجودة العالية مع مقربة من 1400 ملخصات مشروح بشرية.تعتمد البيانات على مربعات اتصال دعم العملاء في العالم الحقيقي وتتضمن ملخصات خارجية ومخفية.نحن نقدم أيضا طريقة تلخيص غير مخالفات جديدة غير محددة
In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue. The goal of the present article is advancing the automation of this task. We introduce the first large scale, high quality, customer care dialog summarization dataset with close to 6500 human annotated summaries. The data is based on real-world customer support dialogs and includes both extractive and abstractive summaries. We also introduce a new unsupervised, extractive summarization method specific to dialogs.
References used
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