[PDF] Top 20 Unsupervised Aspect Based Multi Document Abstractive Summarization

... ion summarization is costly, a line of work has focused on unsupervised ...in unsupervised opinion summarization include both extractive and abstractive ...extractive ... See full document

... Malaysia Airlines said in a statement that flight MH370 had disappeared at 02:40 local time on Saturday after leaving Kuala Lumpur. Southeast Asian states have joined forces to search waters between Malaysia and Vietnam ... See full document

... the abstractive summarization and the other is the extractive ...original document and combines them into a shorter form based on statistical and linguistic features of ...The ... See full document

... transition- based word ordering model, which takes a bag of words, together with optional POS and depen- dency arcs on a subset of input words, yields a sen- tence together with its dependency parse tree that ... See full document

... similarity based method pro- duces automated scores that correlate well with manual pyramid scores, yielding more accurate pyramid scores than string matching based auto- mated methods (Harnly et ... See full document

... We introduce a novel graph-based frame- work for abstractive meeting speech sum- marization that is fully unsupervised and does not rely on any annotations. Our work combines the strengths of ... See full document

... 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 within the same source ... See full document

... the document set, sentence filtering and beam- search are added ...making document model work well as document model may be weak in modeling noisy sentences with rare words or in bad ... See full document

... automatic summarization, centrality is the notion that a summary should contain the core parts of the source ...how summarization can advance past this paradigm towards robust abstraction by making greater ... See full document

... for multi-document summa- rization has not been well ...for multi-document sum- ...method based on KL- divergence cannot prevent redundancy across differ- ent ... See full document

... Extractive summarization and Abstractive ...Extractive summarization deals with identifying the most relevant sentences or passages in one or more documents and combining them together to form a ... See full document

... is based on how much salient information does the summary contain, and readability is based on how fluent and grammat- ical the summary ...of summarization datasets contain the pairs of article with ... See full document

... for abstractive summariza- tion. Unlike traditional document summarization, a model for aspect-based summarization needs to include aspects in its input document ... See full document

... Most abstractive summarization methods (See et al., 2017; Li et al., 2017; Zhou et al., 2017; Liu et al., 2018; Cohan et al., 2018; Paulus et al., 2018) employ sequence-to-sequence (seq2seq) models ... See full document

... the multi-document methods de- scribed above which address data sparsity, re- cent work has attempted unsupervised and weakly supervised methods in non-news domains (Chu and Liu, 2018; Angelidis and ... See full document

... Sentence merging is a technique to create a more informative sentence by merging the information from different source sentences. According to Bing et al., (2015), human summary writers usu- ally merge the important ... See full document

... Zhang et al. (2018) use a latent variable extrac- tive model where sentences are viewed as la- tent variables and sentences with activated vari- ables are used to infer gold summaries. Dong et al. (2018) utilize a policy ... See full document

... Abstractive document summarization seeks to automatically generate a sum- mary for a document, based on some abstract “understanding” of the original ...an unsupervised ... See full document

... an abstractive approach, augment the standard attention-based encoder-decoder RNNs with the ability to copy words from the source text via pointing and to keep track of what has been ...the multi- ... See full document

... Text Summarization has become a very popular Natural Language Processing (NLP) task in recent ...automatic summarization has been developed and improved in order to help users manage all the information ... See full document