Extractive Summarization

We consider the problem of modeling the con- tent structure of texts within a specific do- main, in terms of the topics the texts address and the order in which …...

Scoring sentences in documents given ab- stract summaries created by humans is im- portant in extractive multi-document sum- marization. In this paper, we formulate ex- tractive summarization as a two …...

In this paper, we formulate extractive summarization as a risk minimization problem and propose a unified probabilis- tic framework that naturally combines su- pervised and unsupervised summarization models to inherit …...

In this paper, we investigate how modeling content structure can benefit text analysis ap- plications such as extractive summarization and sentiment analysis. This follows the lin- guistic intuition that rich …...

This paper presents a pilot study of opinion summarization on conversations. We create a corpus containing extractive and abstrac- tive summaries of speaker’s opinion towards a given topic using 88 …...

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 …...

This paper proposes an improved approach to extrac- tive summarization of spoken multi-party interac- tion, in which integrated random walk is performed on a graph constructed on topical/ lexical relations. …...

In this paper, we investigate the use of tempo- ral information for improving extractive sum- marization of historical articles. Our method clusters sentences based on their timestamps and temporal similarity. …...

Multi-dimensional latent text models, such as factorial LDA (f-LDA), capture multiple fac- tors of corpora, creating structured output for researchers to better understand the contents of a corpus. We consider …...

AbstractIn the period since 2004, many novel sophisticated approaches for generic multi-document summarization have been developed. Intuitive simple approaches have also been shown to perform unexpectedly well for the task. …...

A typical discussion thread in an online fo- rum spans multiple pages involving par- ticipation from multiple users and thus, may contain multiple view-points and so- lutions. A user interested …...

Summarizing opinions expressed in online forums can potentially benefit many peo- ple. However, special characteristics of this problem may require changes to stan- dard text summarization techniques. In this work, …...

AbstractExisting sentence regression methods for extractive summarization usually model sentence importance and redundancy in two separate processes. They first evaluate the importance f(s) of each sentence s and then select …...

AbstractPreprocessing is a preliminary step in many fields including IR and NLP. The effect of basic preprocessing settings on English for text summarization is well-studied. However, there is no such …...

Traditional approaches to extractive summarization rely heavily on human- engineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a …...

AbstractTo analyze the limitations and the future directions of the extractive summarization paradigm, this paper proposes an Integer Linear Programming (ILP) formulation to obtain extractive oracle summaries in terms of …...

Due to the rapid-developed Internet and with the big data era coming, the automatic summarization research has been emerged a popular research topic. The aim of automatic summarization is in …...

While extractive summarization is a well stud- ied problem, it is far from solved. In recent years a large number of interesting and com- plex models have been used to …...

AbstractSubmodular maximization with the greedy algorithm has been studied as an effective approach to extractive summarization. This approach is known to have three advantages: its applicability to many useful submodular …...

AbstractQuestion answering (QA) using textual sources for purposes such as reading comprehension (RC) has attracted much attention. This study focuses on the task of explainable multi-hop QA, which requires the …...

AbstractIncorporating related text information has proven successful in stock market prediction. However, it is a huge challenge to utilize texts in the enormous forex (foreign currency exchange) market because the …...

AbstractHighlighting while reading is a natural behavior for people to track salient content of a document. It would be desirable to teach an extractive summarizer to do the same. However, …...

AbstractAutomatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly. Single document summarization (SDS) systems have benefited from advances in …...

AbstractThe recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of why they perform so well, or …...

AbstractExisting models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level. In this paper, we …...

AbstractWe present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides. This is a harder task than the news summarization task, given the …...

AbstractThis paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. Instead of following the commonly used framework of extracting sentences individually and modeling …...

AbstractCommonly adopted metrics for extractive summarization focus on lexical overlap at the token level. In this paper, we present a facet-aware evaluation setup for better assessment of the information coverage …...

AbstractMost current extractive summarization models generate summaries by selecting salient sentences. However, one of the problems with sentence-level extractive summarization is that there exists a gap between the human-written gold …...

AbstractWe propose Implicit Quote Extractor, an end-to-end unsupervised extractive neural summarization model for conversational texts. When we reply to posts, quotes are used to highlight important part of texts. We …...

AbstractIn this paper, we propose a multi-granularity interaction network for extractive and abstractive multi-document summarization, which jointly learn semantic representations for words, sentences, and documents. The word representations are used …...

AbstractMost general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront. As a result, such models are biased on position and often …...

AbstractWe propose encoder-centric stepwise models for extractive summarization using structured transformers – HiBERT and Extended Transformers. We enable stepwise summarization by injecting the previously generated summary into the structured transformer …...

AbstractWith social media becoming popular, a vast of short and noisy messages are produced by millions of users when a hot event happens. Developing social summarization systems becomes more and …...

AbstractRecent efforts to develop deep learning models for text generation tasks such as extractive and abstractive summarization have resulted in state-of-the-art performances on various datasets. However, obtaining the best model …...

AbstractSentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. However, constructing a coherent and informative summary is difficult using a pre-trained BERT-based …...

AbstractWe propose an unsupervised graph-based ranking model for extractive summarization of long scientific documents. Our method assumes a two-level hierarchical graph representation of the source document, and exploits asymmetrical positional …...

AbstractRecent years have brought about an interest in the challenging task of summarizing conversation threads (meetings, online discussions, etc.). Such summaries help analysis of the long text to quickly catch …...

AbstractModeling the relations between text spans in a document is a crucial yet challenging problem for extractive summarization. Various kinds of relations exist among text spans of different granularity, such …...

AbstractSentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a …...

AbstractFine-tuning a large language model on downstream tasks has become a commonly adopted process in the Natural Language Processing (NLP) (CITATION). However, such a process, when associated with the current …...

AbstractTo capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbersome procedures and inefficient …...

State-of-the-art summarization systems are trained and evaluated on massive datasets scraped from the web. Despite their preva- lence, we know very little about the underly- ing characteristics (data noise, summarization …...

AbstractWhen responding to a disaster, humanitarian experts must rapidly process large amounts of secondary data sources to derive situational awareness and guide decision-making. While these documents contain valuable information, manually …...

AbstractNeural-based summarization models suffer from the length limitation of text encoder. Long documents have to been truncated before they are sent to the model, which results in huge loss of …...

AbstractUnsupervised approaches to extractive summarization usually rely on a notion of sentence importance defined by the semantic similarity between a sentence and the document. We propose new metrics of relevance …...

AbstractTraditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives. However, the output summary is always evaluated from summary-level which leads to the …...

AbstractQuery-focused summaries of foreign-language, retrieved documents can help a user understand whether a document is actually relevant to the query term. A standard approach to this problem is to first …...

AbstractThe application of supervised methods to automatic summarization requires the availability of adequate corpora consisting of a set of document-summary pairs. As in most Natural Language Processing tasks, the great …...

AbstractThe IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist’s reasoning and conclusions, and it also aids the referring physician in confirming or …...

AbstractExtractive summarization for long documents is challenging due to the extended structured input context. The long-distance sentence dependency hinders cross-sentence relations modeling, the critical step of extractive summarization. This paper …...

AbstractThe multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently. In one vein of study, researchers are interested in understanding why and how transformers work. In another …...

AbstractSummarization of legal case judgement documents is a challenging problem in Legal NLP. However, not much analyses exist on how different families of summarization models (e.g., extractive vs. abstractive) perform …...

AbstractLabeling large amounts of extractive summarization data is often prohibitive expensive due to time, financial, and expertise constraints, which poses great challenges to incorporating summarization system in practical applications. This …...

AbstractIt has been the norm for a long time to evaluate automated summarization tasks using the popular ROUGE metric. Although several studies in the past have highlighted the limitations of …...

AbstractA release note is a technical document that describes the latest changes to a software product and is crucial in open source software development. However, it still remains challenging to …...

AbstractText segmentation is important for signaling a document’s structure. Without segmenting a long document into topically coherent sections, it is difficult for readers to comprehend the text, let alone find …...

AbstractWith the ever-growing amount of textual data, extractive summarization has become increasingly crucial for efficiently processing information. The TextRank algorithm, a popular unsupervised method, offers excellent potential for this task. …...