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Register a new userTWEETSUMM - A Dialog Summarization Dataset for Customer Service
tweetsumm - بيانات ملخص حوار خدمة العملاء
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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.
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Dialogue summarization has drawn much attention recently. Especially in the customer service domain, agents could use dialogue summaries to help boost their works by quickly knowing customer's issues and service progress. These applications require s
ummaries to contain the perspective of a single speaker and have a clear topic flow structure, while neither are available in existing datasets. Therefore, in this paper, we introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS). CSDS improves the abstractive summaries in two aspects: (1) In addition to the overall summary for the whole dialogue, role-oriented summaries are also provided to acquire different speakers' viewpoints. (2) All the summaries sum up each topic separately, thus containing the topic-level structure of the dialogue. We define tasks in CSDS as generating the overall summary and different role-oriented summaries for a given dialogue. Next, we compare various summarization methods on CSDS, and experiment results show that existing methods are prone to generate redundant and incoherent summaries. Besides, the performance becomes much worse when analyzing the performance on role-oriented summaries and topic structures. We hope that this study could benchmark Chinese dialogue summarization and benefit further studies.
Large-scale pretrained transformer models have demonstrated state-of-the-art (SOTA) performance in a variety of NLP tasks. Nowadays, numerous pretrained models are available in different model flavors and different languages, and can be easily adapte
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