Recent work in neural generation has attracted significant interest in controlling the form of text, such as style, persona, and politeness. However, there has been less work on controlling neural text generation for content. This paper introduces the notion of Content Transfer for long-form text generation, where the task is to generate a next sentence in a document that both fits its context and is grounded in a content-rich external textual source such as a news story. Our experiments on Wikipedia data show significant improvements against competitive baselines. As another contribution of this paper, we release a benchmark dataset of 640k Wikipedia referenced sentences paired with the source articles to encourage exploration of this new task.
Another closely related task is update summarization @cite_14 , where systems attempt to provide a brief summary of the novel information in a new article assuming the user has read a known set of prior documents. Our focus on curating an authoritative resource is a substantial difference. Also our datasets are substantially larger, enabling generative models to be used in this space, where prior update summarization techniques have been primarily extractive @cite_5 @cite_0 .
@cite_0 @cite_5 @cite_14
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Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion. On the other hand, query-based summarization highlights those points that are relevant in the context of a given query. The encode-attend-decode paradigm has achieved notable success in machine translation, extractive summarization, dialog systems, etc. But it suffers from the drawback of generation of repeated phrases. In this work we propose a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions (i) a query attention model (in addition to document attention model) which learns to focus on different portions of the query at different time steps (instead of using a static representation for the query) and (ii) a new diversity based attention model which aims to alleviate the problem of repeating phrases in the summary. In order to enable the testing of this model we introduce a new query-based summarization dataset building on debatepedia. Our experiments show that with these two additions the proposed model clearly outperforms vanilla encode-attend-decode models with a gain of 28 (absolute) in ROUGE-L scores. We present a supervised sentence ranking approach for use in extractive update summarization. We use the same general machine learning approach described in earlier DUC papers, and adapt it to the update summarization task. The system proves adaptable enough to be effective at queryfocused update summaries. The development of online news media grew in number in Indonesia. One technique of news articles summarization is guided summarization where the summary should contain important aspect information. Guided summarization techniques have been developed in the Text Analysis Conference (TAC) 2011 and one of the best methods is SWING by Jun-ping, et al The purpose of this study is to adapt the methods of SWING system to Indonesian news articles as well as integrating with News Aggregator system. In this research, the experiments have purpose to determine the best features and system configuration when adapted to Indonesian news articles. ROUGE-2 and ROUGE-SU4 is used to evaluate the results of the summary where a summary of the system results compared to the human-made summaries. The best system configuration produces summary with evaluation of ROUGE-2 0,31 and ROUGE-SU4 0,22 which is very close to the human-made summaries with a value of ROUGE-2 0,32 and ROUGE-SU4 0.24. In addition, the update summarization component can be run by giving a summary of updates without repeating the information. Adaptation from SWING system to Indonesian news articles is employing features such as sentence length (SL), category relevance score (CRS), category KL-Divergence (CKLD), bigram DFS (BDFS), Top n NE corpus, Top n NE topic, quote sentence removal, and building SVR model for each news category. In many decision-making scenarios, people can benefit from knowing what other people's opinions are. As more and more evaluative documents are posted on the Web, summarizing these useful resources becomes a critical task for many organizations and individuals. This paper presents a framework for summarizing a corpus of evaluative documents about a single entity by a natural language summary. We propose two summarizers: an extractive summarizer and an abstractive one. As an additional contribution, we show how our abstractive summarizer can be modified to generate summaries tailored to a model of the user preferences that is solidly grounded in decision theory and can be effectively elicited from users. We have tested our framework in three user studies. In the first one, we compared the two summarizers. They performed equally well relative to each other quantitatively, while significantly outperforming a baseline standard approach to multidocument summarization. Trends in the results as well as qualitative comments from participants suggest that the summarizers have different strengths and weaknesses. After this initial user study, we realized that the diversity of opinions expressed in the corpus (i.e., its controversiality) might play a critical role in comparing abstraction versus extraction. To clearly pinpoint the role of controversiality, we ran a second user study in which we controlled for the degree of controversiality of the corpora that were summarized for the participants. The outcome of this study indicates that for evaluative text abstraction tends to be more effective than extraction, particularly when the corpus is controversial. In the third user study we assessed the effectiveness of our user tailoring strategy. The results of this experiment confirm that user tailored summaries are more informative than untailored ones.