Top Research Papers on Text Summarization

Analysis of feedback forms filled in after each decision indicated that the intelligibility of present-day machine-generated summaries is high, and the evaluation methods used in the SUMMAC evaluation are of interest to both summarization evaluation as well as evaluation of other ‘output-related’ NLP technologies, where there may be many potentially acceptable outputs.

The TIPSTER Text Summarization Evaluation (SUMMAC) has established definitively that automatic text summarization is very effective in relevance assessment tasks.

This thesis investigates strategies to take user characteristics into account in the summarization process and focuses on the challenges entailed in incorporating knowledge about the user into summarization strategies and providing the user with a text relevant to his needs.

This paper presents a model that uses text categorization and text summarization for searching a document based on user query and an emerging technique for understanding the main purpose of any kind of documents.

This survey intends to investigate some of the most relevant approaches both in the areas of single-document and multiple document summarizations, giving special prominence to experimental methods and extractive techniques.

Text summarization (or rather, automatic text summarization) is the technique where a computer automatically creates an abstract, or summary, of one or more texts. The initial interest in automatic shortening of texts was spawned during the sixties in American research libraries. A large amount of scientific papers and books were to be digitally stored and made searchable. However, the storage capacity was very limited and full papers and books could not be fit into databases those days. Therefore summaries were stored, indexed and made searchable. Sometimes the papers or books already had sum...

A new method for generating extractive summaries directly via unigram and bigram extraction techniques using the selective part of speech tagging to extract significant unigrams and bigrams from a set of sentences is described.

A new method for generating extractive summaries directly via unigram and bigram extraction techniques using the selective part of speech tagging to extract significant unigrams and bigrams from a set of sentences is described.

This paper introduces Summary Explorer, a new tool to support the manual inspection of text summarization systems by compiling the outputs of 55 state-of-the-art single document summarization approaches on three benchmark datasets, and visually exploring them during a qualitative assessment.

This work examines two novel strategiesto approximate SCUs: generating SCU approximations from AMR meaning representations (SMUs) and from large language models (SGUs), respectively and finds that while STUs and SMUs are competitive, the best approximation quality is achieved by SGUs.

This paper studies the development of a web application which summarizes the given input text using different models and its deployment and provides a gist of the corpus enabling comprehension in a timely manner.

Text summarization is the process of identifying the most important meaningful information in a document or set of related documents and compressing them into a shorter version preserving its overall meanings.

This method extracts the key information of the article firstly, and then generates the summarization of the extracted information, and can significantly improve the generative speed compared with generative summarization.

We describe the outline of Text Summarization Challenge (TSC hereafter), a text summarization evaluation conducted as one of the tasks at the NTCIR Workshop2. First, we introduce TSC explaining its background and purpose. Then we describe briefly types of summarization and summarization evaluation methods in general. Next, we focus on TSC, including the participants, the three tasks in TSC, data used, evaluation methods for each task, brief report on the results as well as the features of the Challenge . The future directions for TSC are mentioned at the end as conclusion.

本稿では,要 約手法 として複数の正解に基づ く評価法の提案 を行なった.従 来のテキ ス ト要約の評価方法では唯一の正解 を用 いるが,テ キス トによっては観点の異 なる正 しい要約が複数存在す る場合 もあ り,評 価の信頼性が保証 されないとい う問題があっ た.我 々は,自 動評価 の信頼性 を高めるため,特 に重要文抽出法 に焦点を当てて複数 の正解 に基づ く評価方法 を検討 した.提 案手法では,複 数の正解 と評価対象の要約を 共 にベク トルで表現 し,複 数 の正解の線形結合 と評価対象の要約 との内積の最大値 を 評価値 とする.提 案手法の検証のため に,NTCIR-2要 約データ中の4記 事 に対 して, 要約者7名 で要約の作成 を行 なった.正 解の要約 問の一致度 に基づ く品質評価の結果, 提案手法の評価の正解 として用いるのに十分な品質が得 られなかったが,要 約 の比較 から,照 応 関係,結 束性等,元 テキス ト中の構造を損 なわないように要約する共通の 法則性が見 出され,今 後要約の正解 を作成する上で有用 な知見 を得 た.提 案手法の有 効性 を検証す る予備実験 として,異 なる幾つかの 自動要約手法 と複数正解 との一致度 に基づ く評価 を行 なった.正 解 ごとに評価の高い自動要約手法が異 なる とい う傾向が 見 られ,...

This dissertation proposes a novel framework to produce entity-centric summaries which describe the relationships among input entities and discusses the inherent challenges associated with each module in the framework and presents an evaluation plan.

This paper presents Summary Workbench, a new tool for developing and evaluating text summarization models that can be easily integrated as Docker-based plugins, allowing to examine the quality of their summaries against any input and to evaluate them using various evaluation measures.

A generic text summarization method which uses the latent semantic analysis technique to identify semantically important sentences and two new evaluation methods based on LSA, which measure content similarity between an original document and its summary are proposed.

This work builds an abstract text summarizer for the German language text using the state-of-the-art “Transformer” model and proposes an iterative data augmentation approach which uses synthetic data along with the real summarization data for theGerman language.

A text summarization system that moves beyond standard approaches by using a hybrid approach of linguistic and statistical analysis and by employing text-sort-specific knowledge of document structure and phrases indicating importance is described.

This research uses web scrapping technique that helps in direct extraction of data from online resources hence results in accurate results and concludes that LUHN and LSA are the two best algorithms according to the ROUGE-1 and RouGE-2 scores.

This paper enhances the summarization process with the ability to detect and appropriately treat the text structure and produces a shorter version containing all the main parts of the research.

This work builds an abstract text summarizer for the German text using the state-of-the-art “Transformer” model and proposes an iterative data augmentation approach of using synthetic data additionally along with real German summarization data.

The Sentence Length Impact (SLI) algorithm is proposed, which gives 92% accuracy and translating the same in French, and is proposed to be used for domain based and generic text summarization.

This report will give a brief idea about types of summary, summary evaluation measures and various ways to get summary.

A text summarization approach is proposed based on removal of redundant sentences which is best effective on the documents which are highly redundant and contain repetitive opinions about a topic.

This paper extensively addresses several summarizing strategies depending on intent, volume of data, and outcome.

In order to summarize a story it is necessary to access a high level analysis that highlights the story's central concepts, and a technique of memory representation based on affect units appears to provide the necessary foundation for that analysis.

This article outlines the main lines of research into the creation of automated summaries and its relationships with other areas of linguistic engineering.

This paper proposed various features of Summary Extraction and also analyzed features that are to be applied depending upon the Document.

A conversational platform built on Amazon Alexa Skills is implemented, providing a user with summarized content of a Wikipedia website, and an extractive latent semantic analysis and abstractive discourse-aware attention model are evaluated on publicly available datasets.

An overview of Text Summarization is presented, a very interesting and useful task that gives support to many other tasks as well as it takes advantage of the techniques developed for related Natural Language Processing tasks.

Hierarchical summarization generates a summary for a document based on the hierarchical structure and salient features of the document and user evaluations indicate that hierarchical summarization outperforms traditional summarization.

Text summarization approaches in light of SSHLDA, Vector Space Model and Modified K-Means and Cluster have, to an extent, prevailing with regards to making a powerful summarization of a document.

The base sequence-tosequence model is presented and the latest research that has improved upon it is reviewed, which is the standard method used in machine translations and in sequence-Tosequences prediction, which summarization task is.

This project highlights the utilization of multiple extractive text summarization techniques, including Word Frequency, Lex, Luhn, Kl Summarizer, GPT-2 and BERT, and demonstrates the efficacy of these techniques in generating summaries and assess their quality by comparing them against summaries produced by humans using the specified scoring metrics.

: In this new era

The natural language processing research community is developing new methods for summarizing the text mechanically using automatic text summarization system, which produces a summary of the text.

These methods on the sentence extraction-based text summarization task use the graph based algorithm to calculate importance of each sentence in document and most important sentences are extracted to generate document summary.

Purpose: This article reviews the literature on students’ developing skills in summarizing expository texts and describes strategies for evaluating students’ expository summaries. Evaluation outcomes are presented for a professional development project aimed at helping teachers develop new techniques for teaching summarization. Methods: Strategies for evaluating expository summaries were applied in a professional development project in which teachers learned to teach fourth- and fifth-grade students to identify the macrostructures of short expository texts. Outcomes were measured by comparing ...

The system is based on a sentence scoring algorithm, supported by the emergence of subject-specific word numbering systems, that helps remove irrelevant phrases from the pre-selected resume suggestions, making the suggestions look more like human-generated resumes.

Text Summarization methods can be classified into extractive and abstractive summarization, which are typically based on techniques for sentence extraction and aim to cover the set of sentences that are most important for the overall understanding of a given document.

The procedure of programmed outline incorporates diminishing a content archive by a program so as to make a synopsis that holds the joke of the first content.

This paper proposes to automatically generated summaries of Micro-Blogs conversations dealing with public figures E-Reputation using key-word queries or sample tweet and offer a focused view of the whole Micro-Blog network.

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To solve the optimization problem an adaptive differential evolution with novel mutation strategy is employed and the ROUGE value of summaries got by the proposed approach demonstrated its validity, compared to the traditional methods of sentence selection and the top three performing systems for DUC2001 and DUC2002.

This paper approaches the generation based text summarization problem with a similar class of models, in a relatively smaller data setting and attempts to alleviate the challenges faced with imitation learning.

-Text summarization is the branch of NLP where a computer summarizes a text. A text is entered into the computer, a specific technique is applied

The proposed text ranking approach is based on a graph theoretic ranking model applied to text summarization task and involves ranking the text in the individual coherent chunks and picking the sentences that rank above a given threshold.

To generate a summary for news articles, the goal of automatic news summarization is to provide an excerpt from the news article that covers the news article in fewer sentences and words.