In this demo, we present SciSumm, an interactive multi-document summarization system for scientiﬁc articles. The document collection to be summarized is a list of papers cited together within the same source article, otherwise known as a co-citation. At the heart of the approach is a topic based clustering of fragments extracted from each article based on queries generated from the context surrounding the co-cited list of papers.
Automated summarization methods can be deﬁned as “language-independent,” if they are not based on any languagespeciﬁc knowledge. Such methods can be used for multilingual summarization deﬁned by Mani (2001) as “processing several languages, with summary in the same language as input.” In this paper, we introduce MUSE, a languageindependent approach for extractive summarization based on the linear optimization of several sentence ranking measures using a genetic algorithm.
We design a class of submodular functions meant for document summarization tasks. These functions each combine two terms, one which encourages the summary to be representative of the corpus, and the other which positively rewards diversity. Critically, our functions are monotone nondecreasing and submodular, which means that an efﬁcient scalable greedy optimization scheme has a constant factor guarantee of optimality.
This paper presents a system to summarize a Microblog post and its responses with the goal to provide readers a more constructive and concise set of information for efficient digestion. We introduce a novel two-phase summarization scheme. In the first phase, the post plus its responses are classified into four categories based on the intention, interrogation, sharing, discussion and chat.
Leading text extracts created to support some online Boolean retrieval goals are evaluated for their acceptability as news document summaries. Results are presented and discussed from the perspective of commercial summarization technology needs.
We present a method to automatically generate a concise s u m m a r y by identifying and synthesizing similar elements across related text from a set of multiple documents. Our approach is unique in its usage of language generation to reformulate the wording of the summary. Information overload has created an acute need for summarization. Typically, the same information is described by many different online documents.
A straightforward way for cross-language document summarization is to translate the summary from the source language to the target language by using machine translation services. However, though machine translation techniques have been advanced a lot, the machine translation quality is far from satisfactory, and in many cases, the translated texts are hard to understand.
We propose a novel algorithm for sentiment summarization that takes account of informativeness and readability, simultaneously. Our algorithm generates a summary by selecting and ordering sentences taken from multiple review texts according to two scores that represent the informativeness and readability of the sentence order. The informativeness score is deﬁned by the number of sentiment expressions and the readability score is learned from the target corpus. We evaluate our method by summarizing reviews on restaurants. ...
This paper presents a pilot study of opinion summarization on conversations. We create a corpus containing extractive and abstractive summaries of speaker’s opinion towards a given topic using 88 telephone conversations. We adopt two methods to perform extractive summarization. The ﬁrst one is a sentence-ranking method that linearly combines scores measured from different aspects including topic relevance, subjectivity, and sentence importance.
Extractive methods for multi-document summarization are mainly governed by information overlap, coherence, and content constraints. We present an unsupervised probabilistic approach to model the hidden abstract concepts across documents as well as the correlation between these concepts, to generate topically coherent and non-redundant summaries. Based on human evaluations our models generate summaries with higher linguistic quality in terms of coherence, readability, and redundancy compared to benchmark systems. ...
In citation-based summarization, text written by several researchers is leveraged to identify the important aspects of a target paper. Previous work on this problem focused almost exclusively on its extraction aspect (i.e. selecting a representative set of citation sentences that highlight the contribution of the target paper). Meanwhile, the ﬂuency of the produced summaries has been mostly ignored. For example, diversity, readability, cohesion, and ordering of the sentences included in the summary have not been thoroughly considered. This resulted in noisy and confusing summaries.
Cross-language document summarization is defined as the task of producing a summary in a target language (e.g. Chinese) for a set of documents in a source language (e.g. English). Existing methods for addressing this task make use of either the information from the original documents in the source language or the information from the translated documents in the target language. In this study, we propose to use the bilingual information from both the source and translated documents for this task. ...
We propose a new method for query-oriented extractive multi-document summarization. To enrich the information need representation of a given query, we build a co-occurrence graph to obtain words that augment the original query terms. We then formulate the summarization problem as a Maximum Coverage Problem with Knapsack Constraints based on word pairs rather than single words.
Statistical approaches to automatic text summarization based on term frequency continue to perform on par with more complex summarization methods. To compute useful frequency statistics, however, the semantically important words must be separated from the low-content function words. The standard approach of using an a priori stopword list tends to result in both undercoverage, where syntactical words are seen as semantically relevant, and overcoverage, where words related to content are ignored. ...
Comparative News Summarization aims to highlight the commonalities and differences between two comparable news topics. In this study, we propose a novel approach to generating comparative news summaries. We formulate the task as an optimization problem of selecting proper sentences to maximize the comparativeness within the summary and the representativeness to both news topics. We consider semantic-related cross-topic concept pairs as comparative evidences, and consider topic-related concepts as representative evidences....
This paper presents a model for summarizing multiple untranscribed spoken documents. Without assuming the availability of transcripts, the model modiﬁes a recently proposed unsupervised algorithm to detect re-occurring acoustic patterns in speech and uses them to estimate similarities between utterances, which are in turn used to identify salient utterances and remove redundancies.
Online reviews are often accompanied with numerical ratings provided by users for a set of service or product aspects. We propose a statistical model which is able to discover corresponding topics in text and extract textual evidence from reviews supporting each of these aspect ratings – a fundamental problem in aspect-based sentiment summarization (Hu and Liu, 2004a). Our model achieves high accuracy, without any explicitly labeled data except the user provided opinion ratings.
Different summarization requirements could make the writing of a good summary more difﬁcult, or easier. Summary length and the characteristics of the input are such constraints inﬂuencing the quality of a potential summary. In this paper we report the results of a quantitative analysis on data from large-scale evaluations of multi-document summarization, empirically conﬁrming this hypothesis.
In this paper, we propose a novel ranking framework – Co-Feedback Ranking (CoFRank), which allows two base rankers to supervise each other during the ranking process by providing their own ranking results as feedback to the other parties so as to boost the ranking performance. The mutual ranking refinement process continues until the two base rankers cannot learn from each other any more. The overall performance is improved by the enhancement of the base rankers through the mutual learning mechanism.
We present BAYE S UM (for “Bayesian summarization”), a model for sentence extraction in query-focused summarization. BAYE S UM leverages the common case in which multiple documents are relevant to a single query. Using these documents as reinforcement for query terms, BAYE S UM is not afﬂicted by the paucity of information in short queries. We show that approximate inference in BAYE S UM is possible on large data sets and results in a stateof-the-art summarization system.