Multi Document Summarization

: This paper 1 develops a method for combining query- relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce …...

We present and evaluate the initial version of RIPTIDES, a system that combines information extraction, extraction-based summarization, and natural language generation to support user- directed multidocument summarization. 1....

The problem of organizing information for m ultido cumen t summarization so that the generated summary is coheren t has receiv ed relativ ely little atten tion. In this pap …...

For multi-document summarization where docu- ments are collected over an extended period of time, the subject in a document changes over time. This paper focuses on subject shift and presents …...

In this paper, we explore the use of automatic syntactic simplification for improving content selection in multi-document summarization. In particular, we show how simplifying parenthet- icals by removing relative clauses …...

In this paper, we use the information re- dundancy in multilingual input to correct errors in machine translation and thus im- prove the quality of multilingual sum- maries. We consider …...

Ordering information is a difficult but important task for applications generat- ing natural-language text. We present a bottom-up approach to arranging sen- tences extracted for multi-document sum- marization. To capture …...

Many systems for tasks such as question answer- ing, multi-document summarization, and infor- mation retrieval need robust numerical measures of lexical relatedness. Standard thesaurus-based measures of word pair similarity are …...

The graph-based ranking algorithm has been recently exploited for multi-document sum- marization by making only use of the sen- tence-to-sentence relationships in the documents, under the assumption that all the …...

Different summarization requirements could make the writing of a good summary more dif- ficult, or easier. Summary length and the char- acteristics of the input are such constraints in- fluencing …...

This paper presents a novel approach to au- tomatic captioning of toponym-referenced im- ages. The automatic captioning procedure works by summarizing multiple web-documents that contain information related to an image’s …...

This paper presents a framework for au- tomatically processing information com- ing from community Question Answering (cQA) portals with the purpose of gen- erating a trustful, complete, relevant and succinct …...

Multi-document summarization has been an important problem in infor- mation retrieval. It aims to dis- till the most important information from a set of documents to gener- ate a compressed …...

Multi-document summarization aims to produce a concise summary that contains salient information from a set of source documents. In this field, sentence ranking has hitherto been the issue of most …...

Single-document summarization and multi- document summarization are very closely re- lated tasks and they have been widely investi- gated independently. This paper examines the mutual influences between the two tasks …...

Extractive methods for multi-document sum- marization are mainly governed by informa- tion overlap, coherence, and content con- straints. We present an unsupervised proba- bilistic approach to model the hidden abstract …...

Unsupervised approaches to multi-document summarization consist of two steps: find- ing a content model of the documents to be summarized, and then generating a summary that best represents the most …...

We learn a joint model of sentence extraction and compression for multi-document summa- rization. Our model scores candidate sum- maries according to a combined linear model whose features factor over …...

We present a novel unsupervised approach to the problem of multi-document summariza- tion of scientific articles, in which the doc- ument collection is a list of papers cited to- gether …...

Multi-document summarization involves many aspects of content selection and sur- face realization. The summaries must be informative, succinct, grammatical, and obey stylistic writing conventions. We present a method where such …...

Subjectivity analysis has been actively used in various applications such as opinion mining of customer reviews in online review sites, question-answering in CQA sites, multi-document summarization, etc. However, there has …...

AbstractSupervised learning methods and LDA based topic model have been successfully applied in the field of multi-document summarization. In this paper, we propose a novel supervised approach that can incorporate …...

We consider the problem of using sentence compression techniques to facilitate query- focused multi-document summarization. We present a sentence-compression-based frame- work for the task, and design a series of learning-based …...

Generative probabilistic models have been used for content modelling and template induction, and are typically trained on small corpora in the target domain. In contrast, vector space models of distribu- …...

ive Multi-Document Summarization: A Caseframe Analysis of Centrality and Domain Jackie Chi Kit Cheung University of Toronto 10 King’s College Rd., Room 3302 Toronto, ON, Canada M5S 3G4 jcheung@cs.toronto.edu Gerald …...

In this study, we analyzed the effects of ap- plying different levels of stemming approaches such as fixed-length word truncation and mor- phological analysis for multi-document sum- marization (MDS) on …...

In this paper, we investigate a challeng- ing task of automatic related work gener- ation. Given multiple reference papers as input, the task aims to generate a related work section …...

We study the use of temporal information in the form of timelines to enhance multi- document summarization. We employ a fully automated temporal processing sys- tem to generate a timeline …...

This paper explores alternate algorithms, reward functions and feature sets for per- forming multi-document summarization using reinforcement learning with a high focus on reproducibility. We show that ROUGE results can …...

Multi-document summarization (MDS) systems have been designed for short, un- structured summaries of 10-15 documents, and are inadequate for larger document collections. We propose a new approach to scaling up …...

In this paper, we focus on the problem of using sentence compression techniques to improve multi-document summariza- tion. We propose an innovative sentence compression method by considering every node in …...

Paraphrase generation is widely used for various natural language processing (NLP) applications such as question answering, multi-document summarization, and machine translation. In this study, we identify the problems occurring in …...

AbstractIn this paper we consider a method for extraction of sets of semantically similar language expressions representing different partici-pants of the text story ― thematic chains. The method is based …...

This paper describes a method of multi- document summarization with evolutionary computation. In automatic document sum- marization, the method to make a summary by finding the best combination of important …...

ive Multi-document Summarization with Semantic Infor- mation Extraction Wei Li Key Lab of Intelligent Info. Processing, Institute of Computing Technology, CAS Beijing, 100190, China weili@kg.ict.ac.cn Abstract This paper proposes a …...

Multi-document Summarization (MDS) is of great value to many real world applications. Many scoring models are proposed to select appropriate sentences from documents to form the summary, in which the …...

The increasing amount of online content mo- tivated the development of multi-document summarization methods. In this work, we explore straightforward approaches to extend single-document summarization methods to multi-document summarization. The …...

We present a novel framework of system combination for multi-document summarization. For each input set (input), we generate candidate summaries by combining whole sentences from the summaries generated by different …...

AbstractIn the age of information exploding, multi-document summarization is attracting particular attention for the ability to help people get the main ideas in a short time. Traditional extractive methods simply …...

Temporal common sense has applications in AI tasks such as QA, multi-document summa- rization, and human-AI communication. We propose the task of sequencing – given a jum- bled set of …...

AbstractResearch in multi-document summarization has focused on newswire corpora since the early beginnings. However, the newswire genre provides genre-specific features such as sentence position which are easy to exploit in …...

AbstractMulti-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinity-preserving random walk to the summarization task, which preserves …...

AbstractConcept maps can be used to concisely represent important information and bring structure into large document collections. Therefore, we study a variant of multi-document summarization that produces summaries in the …...

AbstractWe propose a neural multi-document summarization system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural …...

AbstractIn this paper we investigate a new task of automatically constructing an overview article from a given set of news articles about a news event. We propose a news synthesis …...

AbstractMulti-document summarization (MDS) aims at producing a good-quality summary for several related documents. In this paper, we propose a spectral-based hypothesis, which states that the goodness of summary candidate is …...

AbstractSingle-document and multi-document summarizations are very closely related in both task definition and solution method. In this work, we propose to improve neural abstractive multi-document summarization by jointly learning an …...

AbstractRecent work has shown that pre-trained Transformers obtain remarkable performance on many natural language processing tasks including automatic summarization. However, most work has focused on (relatively) data-rich single-document summarization settings. …...

AbstractToday’s research progress in the field of multi-document summarization is obstructed by the small number of available datasets. Since the acquisition of reference summaries is costly, existing datasets contain only …...

AbstractWhile neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (MDS). We observe two major challenges when adapting SDS advances …...

AbstractMulti-document summarization is a challenging task for which there exists little large-scale datasets. We propose Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document …...

AbstractWhen summarizing a collection of views, arguments or opinions on some topic, it is often desirable not only to extract the most salient points, but also to quantify their prevalence. …...

AbstractMost work on multi-document summarization has focused on generic summarization of information present in each individual document set. However, the under-explored setting of update summarization, where the goal is to …...

AbstractIn recent years, reference-based and supervised summarization evaluation metrics have been widely explored. However, collecting human-annotated references and ratings are costly and time-consuming. To avoid these limitations, we propose a …...

AbstractShort textual descriptions of entities provide summaries of their key attributes and have been shown to be useful sources of background knowledge for tasks such as entity linking and question …...

AbstractThis paper presents an efficient graph-enhanced approach to multi-document summarization (MDS) with an encoder-decoder Transformer model. This model is based on recent advances in pre-training both encoder and decoder on …...

ive Multi-Document Summarization Shuaiqi LIU, Jiannong Cao, Ruosong Yang, and Zhiyuan Wen Department of Computing, The Hong Kong Polytechnic University {cssqliu, csjcao, csryang, cszwen}@comp.polyu.edu.hk Abstract Abstractive multi-document summarization aims to …...

AbstractIn recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online …...

ive Multi-Document Summarization Hao Zhou1 , Weidong Ren1 , Gongshen Liu1∗, Bo Su1 , Wei Lu2 1 School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University 2 StatNLP …...

AbstractMulti-text applications, such as multi-document summarization, are typically required to model redundancies across related texts. Current methods confronting consolidation struggle to fuse overlapping information. In order to explicitly represent content …...

AbstractMulti-source sequence generation (MSG) is an important kind of sequence generation tasks that takes multiple sources, including automatic post-editing, multi-source translation, multi-document summarization, etc. As MSG tasks suffer from the …...

AbstractWikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, previous works generally view the abstract as …...

AbstractMulti-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models. However, a standing limitation of these …...

AbstractA notable challenge in Multi-Document Summarization (MDS) is the extremely-long length of the input. In this paper, we present an extract-then-abstract Transformer framework to overcome the problem. Specifically, we leverage …...

AbstractMulti-Document Summarization (MDS) commonly employs the 2-stage extract-then-abstract paradigm, which first extracts a relatively short meta-document, then feeds it into the deep neural networks to generate an abstract. Previous work …...

AbstractEvaluating multi-document summarization (MDS) quality is difficult. This is especially true in the case of MDS for biomedical literature reviews, where models must synthesize contradicting evidence reported across different documents. …...

AbstractInformation extraction (IE) and summarization are closely related, both tasked with presenting a subset of the information contained in a natural language text. However, while IE extracts structural representations, summarization …...

AbstractMulti-document summarization (MDS) is a difficult task in Natural Language Processing, aiming to summarize information from several documents. However, the source documents are often insufficient to obtain a qualitative summary. …...