MEAD - A Platform for Multidocument Multilingual Text Summarization

By Dragomir R. Radev, Timothy B. Allison, Sasha Blair-Goldensohn, John Blitzer, Arda Çelebi, Stanko Dimitrov, Elliott Franco Drábek, Ali Hakim, Wai Lam, Danyu Liu and 7 more. 2004

406 citations recorded by the source corpus.

Abstract This paper describes the functionality of MEAD, a comprehensive, public domain, open source, multidocument multilingual summarization environment that has been thus far downloaded by more than 500 organizations. MEAD has been used in a variety of summarization applications ranging from summarization for mobile devices to Web page summarization within a search engine and to novelty detection.

Subfields: Natural Language Processing

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Citations in the corpus

Recursively Summarizing Books with Human Feedback (2021, methodology): “…is challenging (Mihalcea and Ceylan, 2007; Ladhak et al., 2020; Kry´sci´nski et al., 2021) and existing methods are typically either extractive (Radev et al., 2004; Mihalcea and Tarau, 2004; Bamman and Smith, 2013; Ladhak et al., 2020) or focused on shorter stories (Kazantseva, 2006; Zhang…”
Neural Summarization by Extracting Sentences and Words (2016, background): “These include surface features such as sentence position and length (Radev et al., 2004), the words in the title, the presence of proper nouns, content features such as word frequency (Nenkova et al.”
HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization (2019, background): “Early work with these models above mostly leverage human engineered features such as sentence position and length (Radev et al., 2004), word frequency (Nenkova et al., 2006) and event features (Filatova and Hatzivassiloglou, 2004b).”
Coherent Citation-Based Summarization of Scientific Papers (2011, background): “The second baseline is the MEAD summarizer (Radev et al., 2004) with all its settings set to default.”
A Survey of Text Summarization Techniques (2012, background): “Many have adopted this seminal approach, mostly in its generic version, sometimes using different measures of novelty to select new sentences [91, 101, 65].”
Im2Text: Describing Images Using 1 Million Captioned Photographs (2011, unknown): “Bad”
A Survey of Text Summarization Extractive Techniques (2010, methodology): “MEAD [26] is the multi-lingual summarization and evaluation method.”
Graph-based Neural Multi-Document Summarization (2017, background): “First, they utilize human-engineered features such as sentence position and length (Radev et al., 2004a), word frequency and importance (Nenkova et al., 2006; Hong and Nenkova, 2014), among others, to rank sentence salience.”
Neural Extractive Summarization with Side Information (2017, background): “Despite recent improvements, abstractive systems still struggle to outperform extractive systems.”
Automatic Text Summarization: Past, Present and Future (2013, background): “Summarization in a cross-lingual environment was studied in the 2001 Johns Hopkins research workshop [65] where evaluation resources and summarization algorithms for English and Chinese were developed [54].”
A Supervised Approach to Extractive Summarisation of Scientific Papers (2017, background): “, 2017), sentence length (Radev et al., 2004), words in the title, presence of proper nouns, word frequency (Nenkova et al.”
Opinion Mining and Sentiment Analysis (2008, methodology): “They compare this system against a modification of an existing sentence-extraction system, MEAD [247].”
New classification models for detecting Hate and Violence web content (2015, methodology): “Here we apply the Centroid method based MEAD summarization tool (Radev et al, 2004) to the Hate and Violence web page collections separately.”