extractive summarization
A Redundancy Aware Sentence Regression Framework for Extractive Summarization
... above extractive summarization methods, redundancy is mainly considered in two ...the summarization task and models redundancy from the perspective of this ...
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Ranking Sentences for Extractive Summarization with Reinforcement Learning
... tive summarization to rank sentences for summary ...helps extractive summarization in two ways: (a) it directly optimizes the evaluation met- ric instead of maximizing the likelihood of the ...
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Extractive Summarization with SWAP NET: Sentences and Words from Alternating Pointer Networks
... We present SWAP-NET, a neural sequence-to- sequence model for extractive summarization that outperforms state-of-the-art extractive summariz- ers SummaRuNNer (Nallapati et al., 2017) and NN (Cheng ...
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Exploiting Discourse Level Segmentation for Extractive Summarization
... Extractive summarization selects and concate- nates the most essential text spans in a docu- ...improves extractive summarization per- formance when content selection is modeled through two ...
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Indicative Tweet Generation: An Extractive Summarization Problem?
... an extractive summarization setting, it is unclear to what extent ac- tual indicative tweets behave like extrac- tive ...as extractive summarization, and point to the need for the development ...
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Guiding Extractive Summarization with Question Answering Rewards
... Highlighting while reading is a natural behav- ior for people to track salient content of a doc- ument. It would be desirable to teach an ex- tractive summarizer to do the same. However, a major obstacle to the ...
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Extractive Summarization under Strict Length Constraints
... single-document, extractive summarization has been conducted since the 1950s (Luhn, ...ditionally, extractive single document summarization has focused on scoring, ranking, and extracting the ...
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Extractive Summarization by Maximizing Semantic Volume
... compressive summarization, by simply including compressed sentences in the embedded space and running Algorithm 1 without any ...the summarization methods that jointly extracts and compresses ...
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Automatic Punjabi Text Extractive Summarization System
... Text Summarization is condensing the source text into shorter form and retaining its information content and overall ...text Summarization system is text extraction based summarization system which ...
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Self Supervised Learning for Contextualized Extractive Summarization
... Extractive summarization aims at shortening the original article while retaining the key information through the way of selection sentences from the original ...
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Reinforced Extractive Summarization with Question Focused Rewards
... We investigate a new training paradigm for extractive summarization. Traditionally, human abstracts are used to derive gold- standard labels for extraction units. How- ever, the labels are often inaccurate, ...
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Learning Summary Prior Representation for Extractive Summarization
... Sentence ranking, the vital part of extractive summarization, has been extensively investigated. Regardless of ranking models (Osborne, 2002; Galley, 2006; Conroy et al., 2004; Li et al., 2007), feature ...
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BanditSum: Extractive Summarization as a Contextual Bandit
... modeling extractive summarization than the sequential bi- nary labeling setting, especially in the cases when good summary sentences appear later in the doc- ...
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Extractive Summarization Using Supervised and Semi Supervised Learning
... for extractive summarization, such as signature word, event and sentence rele- ...based summarization, but the new emerging features are not concerned, such as event features (Li ...
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A Learning based Sampling Approach to Extractive Summarization
... Annotators wrote abstractive summaries for each meeting and then linked summary sentences to those DA segments from the meeting transcripts which best conveyed the information in the ab- stracts. There was no limit on ...
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Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization
... Just like their convex counterparts in the continu- ous case, submodular functions share unique prop- erties that make them conveniently optimizable. For this reason, they are are popular and have been applied to a ...
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The Role of Discourse Units in Near Extractive Summarization
... Compressive summarization. To explore the utility of EDUs in summarization, we examine near-extractive summaries in the NYT corpus which are drawn from sentences in the document but omit at least one ...
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Discovery of Topically Coherent Sentences for Extractive Summarization
... we utilize the advantages of previous topic models and build an unsupervised generative model that can associate each word in each document with three random variables: a sentence S, a higher-level topic H, and a ...
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Subtree Extractive Summarization via Submodular Maximization
... under multiple linear constraints with a perfor- mance guarantee 1 − e − 1 in polynomial time. Al- though their approach can represent more flexible constraints, we cannot use their algorithm to solve our problem, ...
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Topical Coherence for Graph based Extractive Summarization
... We introduce a completely unsupervised graph- based summarization using latent drichlet alloca- tion (LDA, Blei and Lafferty (2009)). LDA is a simple model for topic modeling where topic probabilities are assigned ...
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