Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback
نویسندگان
چکیده
In this paper, we propose an extractive multi-document summarization (MDS) system using joint optimization and active learning for content selection grounded in user feedback. Our method interactively obtains user feedback to gradually improve the results of a state-of-the-art integer linear programming (ILP) framework for MDS. Our methods complement fully automatic methods in producing highquality summaries with a minimum number of iterations and feedbacks. We conduct multiple simulation-based experiments and analyze the effect of feedbackbased concept selection in the ILP setup in order to maximize the user-desired content in the summary.
منابع مشابه
Web pages ranking algorithm based on reinforcement learning and user feedback
The main challenge of a search engine is ranking web documents to provide the best response to a user`s query. Despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. In this paper, a ranking algorithm based on the reinforcement le...
متن کاملRRLUFF: Ranking function based on Reinforcement Learning using User Feedback and Web Document Features
Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the rank...
متن کاملBandwidth and Delay Optimization by Integrating of Software Trust Estimator with Multi-User Cloud Resource Competence
Trust Establishment is one of the significant resources to enhance the scalability and reliability of resources in the cloud environment. To establish a novel trust model on SaaS (Software as a Service) cloud resources and to optimize the resource utilization of multiple user requests, an integrated software trust estimator with multi-user resource competence (IST-MRC) optimization mechanism is...
متن کاملبازیابی تعاملی تصاویر طبیعت با بهره گیری از یادگیری چند نمونه ای
Content-based image retrieval (CBIR) has received considerable research interest in the recent years. The basic problem in CBIR is the semantic gap between the high-level image semantics and the low-level image features. Region-based image retrieval and learning from user interaction through relevance feedback are two main approaches to solving this problem. Recently, the research in integra...
متن کاملiDVS: An Interactive Multi-document Visual Summarization System
Multi-document summarization is a fundamental tool for understanding documents. Given a collection of documents, most of existing multidocument summarization methods automatically generate a static summary for all the users using unsupervised learning techniques such as sentence ranking and clustering. However, these methods almost exclude human from the summarization process. They do not allow...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017