Social media posts are a great source for life summaries aggregating activities, events, interactions and thoughts of the last months or years. They can be used for personal reminiscence as well as for keeping track with developments in the lives of not-so-close friends. One of the core challenges of automatically creating such summaries is to decide which posts are memorable, i.e., should be considered for retention and which ones to forget. To address this challenge, we design and conduct user evaluation studies and construct a corpus that captures human expectations towards content retention. We analyze this corpus to identify a small set of seed features that are most likely to characterize memorable posts. Next, we compile a broader set of features that are leveraged to build general and personalized machine-learning models to rank posts for retention. By applying feature selection, we identify a compact yet effective subset of these features. The models trained with the presented feature sets outperform the baseline models exploiting an intuitive set of temporal and social features.

中文翻译：

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那是过去的日子：学习对社交媒体帖子进行排名以使人联想到

社交媒体帖子是汇总过去几个月或几年中的活动，事件，互动和思想的生活摘要的重要来源。它们可用于个人回忆，以及跟踪不太亲密朋友的生活发展。自动创建此类摘要的核心挑战之一是确定哪些帖子值得纪念，即应考虑保留哪些帖子，而忘记哪些帖子。为了应对这一挑战，我们设计并进行了用户评估研究，并构建了一个语料库，以捕捉人类对内容保留的期望。我们分析该语料库，以识别出最有可能刻画难忘职位的一小组种子特征。下一个，我们会编译更广泛的功能，这些功能可用于构建通用的和个性化的机器学习模型，以对职位进行保留排名。通过应用特征选择，我们确定了这些特征的紧凑而有效的子集。通过使用直观的时间和社交功能集，使用呈现的功能集训练的模型优于基线模型。