-
Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996)
0.02
0.021080218 = product of:
0.08432087 = sum of:
0.08432087 = product of:
0.12648131 = sum of:
0.08090435 = weight(_text_:language in 6751) [ClassicSimilarity], result of:
0.08090435 = score(doc=6751,freq=4.0), product of:
0.16497234 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.042049456 = queryNorm
0.4904116 = fieldWeight in 6751, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.0625 = fieldNorm(doc=6751)
0.045576964 = weight(_text_:22 in 6751) [ClassicSimilarity], result of:
0.045576964 = score(doc=6751,freq=2.0), product of:
0.14725003 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.042049456 = queryNorm
0.30952093 = fieldWeight in 6751, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=6751)
0.6666667 = coord(2/3)
0.25 = coord(1/4)
- Abstract
- Presents a system for summarizing quantitative data in natural language, focusing on the use of a corpus of basketball game summaries, drawn from online news services, to empirically shape the system design and to evaluate the approach. Initial corpus analysis revealed characteristics of textual summaries that challenge the capabilities of current language generation systems. A revision based corpus analysis was used to identify and encode the revision rules of the system. Presents a quantitative evaluation, using several test corpora, to measure the robustness of the new revision based model
- Date
- 6. 3.1997 16:22:15
-
Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023)
0.01
0.010706769 = product of:
0.042827077 = sum of:
0.042827077 = product of:
0.06424061 = sum of:
0.03575501 = weight(_text_:language in 1012) [ClassicSimilarity], result of:
0.03575501 = score(doc=1012,freq=2.0), product of:
0.16497234 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.042049456 = queryNorm
0.21673335 = fieldWeight in 1012, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.0390625 = fieldNorm(doc=1012)
0.028485604 = weight(_text_:22 in 1012) [ClassicSimilarity], result of:
0.028485604 = score(doc=1012,freq=2.0), product of:
0.14725003 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.042049456 = queryNorm
0.19345059 = fieldWeight in 1012, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=1012)
0.6666667 = coord(2/3)
0.25 = coord(1/4)
- Abstract
- With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.
- Date
- 22. 6.2023 14:55:20
-
Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016)
0.01
0.009538297 = product of:
0.038153186 = sum of:
0.038153186 = product of:
0.05722978 = sum of:
0.028744178 = weight(_text_:29 in 2640) [ClassicSimilarity], result of:
0.028744178 = score(doc=2640,freq=2.0), product of:
0.14791684 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.042049456 = queryNorm
0.19432661 = fieldWeight in 2640, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.0390625 = fieldNorm(doc=2640)
0.028485604 = weight(_text_:22 in 2640) [ClassicSimilarity], result of:
0.028485604 = score(doc=2640,freq=2.0), product of:
0.14725003 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.042049456 = queryNorm
0.19345059 = fieldWeight in 2640, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=2640)
0.6666667 = coord(2/3)
0.25 = coord(1/4)
- Date
- 22. 1.2016 12:29:41
-
Pinto, M.: Engineering the production of meta-information : the abstracting concern (2003)
0.01
0.009485095 = product of:
0.03794038 = sum of:
0.03794038 = product of:
0.113821134 = sum of:
0.113821134 = weight(_text_:29 in 4667) [ClassicSimilarity], result of:
0.113821134 = score(doc=4667,freq=4.0), product of:
0.14791684 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.042049456 = queryNorm
0.7694941 = fieldWeight in 4667, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.109375 = fieldNorm(doc=4667)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Date
- 27.11.2005 18:29:55
- Source
- Journal of information science. 29(2003) no.5, S.405-418
-
Kannan, R.; Ghinea, G.; Swaminathan, S.: What do you wish to see? : A summarization system for movies based on user preferences (2015)
0.01
0.008599891 = product of:
0.034399565 = sum of:
0.034399565 = product of:
0.051599346 = sum of:
0.028604005 = weight(_text_:language in 2683) [ClassicSimilarity], result of:
0.028604005 = score(doc=2683,freq=2.0), product of:
0.16497234 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.042049456 = queryNorm
0.17338668 = fieldWeight in 2683, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.03125 = fieldNorm(doc=2683)
0.022995342 = weight(_text_:29 in 2683) [ClassicSimilarity], result of:
0.022995342 = score(doc=2683,freq=2.0), product of:
0.14791684 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.042049456 = queryNorm
0.15546128 = fieldWeight in 2683, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.03125 = fieldNorm(doc=2683)
0.6666667 = coord(2/3)
0.25 = coord(1/4)
- Abstract
- Video summarization aims at producing a compact version of a full-length video while preserving the significant content of the original video. Movie summarization condenses a full-length movie into a summary that still retains the most significant and interesting content of the original movie. In the past, several movie summarization systems have been proposed to generate a movie summary based on low-level video features such as color, motion, texture, etc. However, a generic summary, which is common to everyone and is produced based only on low-level video features will not satisfy every user. As users' preferences for the summary differ vastly for the same movie, there is a need for a personalized movie summarization system nowadays. To address this demand, this paper proposes a novel system to generate semantically meaningful video summaries for the same movie, which are tailored to the preferences and interests of a user. For a given movie, shots and scenes are automatically detected and their high-level features are semi-automatically annotated. Preferences over high-level movie features are explicitly collected from the user using a query interface. The user preferences are generated by means of a stored-query. Movie summaries are generated at shot level and scene level, where shots or scenes are selected for summary skim based on the similarity measured between shots and scenes, and the user's preferences. The proposed movie summarization system is evaluated subjectively using a sample of 20 subjects with eight movies in the English language. The quality of the generated summaries is assessed by informativeness, enjoyability, relevance, and acceptance metrics and Quality of Perception measures. Further, the usability of the proposed summarization system is subjectively evaluated by conducting a questionnaire survey. The experimental results on the performance of the proposed movie summarization approach show the potential of the proposed system.
- Date
- 25. 1.2016 18:45:29
-
Moens, M.F.: Automatic indexing and abstracting of document texts (2000)
0.01
0.008427536 = product of:
0.033710144 = sum of:
0.033710144 = product of:
0.10113043 = sum of:
0.10113043 = weight(_text_:language in 6892) [ClassicSimilarity], result of:
0.10113043 = score(doc=6892,freq=4.0), product of:
0.16497234 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.042049456 = queryNorm
0.6130145 = fieldWeight in 6892, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.078125 = fieldNorm(doc=6892)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Content
- Need for indexing and abstracting texts; attributes of texts; text representations and their use; selection of natural language index terms; assignment of controlled language index texts; automatic abstracting; applications
-
Soricut, R.; Marcu, D.: Abstractive headline generation using WIDL-expressions (2007)
0.01
0.0072251074 = product of:
0.02890043 = sum of:
0.02890043 = product of:
0.08670129 = sum of:
0.08670129 = weight(_text_:language in 943) [ClassicSimilarity], result of:
0.08670129 = score(doc=943,freq=6.0), product of:
0.16497234 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.042049456 = queryNorm
0.5255505 = fieldWeight in 943, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.0546875 = fieldNorm(doc=943)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Abstract
- We present a new paradigm for the automatic creation of document headlines that is based on direct transformation of relevant textual information into well-formed textual output. Starting from an input document, we automatically create compact representations of weighted finite sets of strings, called WIDL-expressions, which encode the most important topics in the document. A generic natural language generation engine performs the headline generation task, driven by both statistical knowledge encapsulated in WIDL-expressions (representing topic biases induced by the input document) and statistical knowledge encapsulated in language models (representing biases induced by the target language). Our evaluation shows similar performance in quality with a state-of-the-art, extractive approach to headline generation, and significant improvements in quality over previously proposed solutions to abstractive headline generation.
-
McKeown, K.; Robin, J.; Kukich, K.: Generating concise natural language summaries (1995)
0.01
0.005959168 = product of:
0.023836672 = sum of:
0.023836672 = product of:
0.07151002 = sum of:
0.07151002 = weight(_text_:language in 2932) [ClassicSimilarity], result of:
0.07151002 = score(doc=2932,freq=2.0), product of:
0.16497234 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.042049456 = queryNorm
0.4334667 = fieldWeight in 2932, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.078125 = fieldNorm(doc=2932)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
-
Salton, G.; Allan, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine readable texts (1994)
0.00
0.0047906963 = product of:
0.019162785 = sum of:
0.019162785 = product of:
0.057488356 = sum of:
0.057488356 = weight(_text_:29 in 1949) [ClassicSimilarity], result of:
0.057488356 = score(doc=1949,freq=2.0), product of:
0.14791684 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.042049456 = queryNorm
0.38865322 = fieldWeight in 1949, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.078125 = fieldNorm(doc=1949)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Date
- 16. 8.1998 12:30:29
-
Liu, J.; Wu, Y.; Zhou, L.: ¬A hybrid method for abstracting newspaper articles (1999)
0.00
0.004767334 = product of:
0.019069336 = sum of:
0.019069336 = product of:
0.05720801 = sum of:
0.05720801 = weight(_text_:language in 4059) [ClassicSimilarity], result of:
0.05720801 = score(doc=4059,freq=2.0), product of:
0.16497234 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.042049456 = queryNorm
0.34677336 = fieldWeight in 4059, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.0625 = fieldNorm(doc=4059)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Abstract
- This paper introduces a hybrid method for abstracting Chinese text. It integrates the statistical approach with language understanding. Some linguistics heuristics and segmentation are also incorporated into the abstracting process. The prototype system is of a multipurpose type catering for various users with different reqirements. Initial responses show that the proposed method contributes much to the flexibility and accuracy of the automatic Chinese abstracting system. In practice, the present work provides a path to developing an intelligent Chinese system for automating the information
-
Craven, T.C.: ¬A phrase flipper for the assistance of writers of abstracts and other text (1995)
0.00
0.003832557 = product of:
0.015330228 = sum of:
0.015330228 = product of:
0.045990683 = sum of:
0.045990683 = weight(_text_:29 in 4897) [ClassicSimilarity], result of:
0.045990683 = score(doc=4897,freq=2.0), product of:
0.14791684 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.042049456 = queryNorm
0.31092256 = fieldWeight in 4897, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.0625 = fieldNorm(doc=4897)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Date
- 17. 8.1996 10:29:59
-
Goh, A.; Hui, S.C.: TES: a text extraction system (1996)
0.00
0.0037980804 = product of:
0.0151923215 = sum of:
0.0151923215 = product of:
0.045576964 = sum of:
0.045576964 = weight(_text_:22 in 6599) [ClassicSimilarity], result of:
0.045576964 = score(doc=6599,freq=2.0), product of:
0.14725003 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.042049456 = queryNorm
0.30952093 = fieldWeight in 6599, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=6599)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Date
- 26. 2.1997 10:22:43
-
Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996)
0.00
0.0037980804 = product of:
0.0151923215 = sum of:
0.0151923215 = product of:
0.045576964 = sum of:
0.045576964 = weight(_text_:22 in 6974) [ClassicSimilarity], result of:
0.045576964 = score(doc=6974,freq=2.0), product of:
0.14725003 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.042049456 = queryNorm
0.30952093 = fieldWeight in 6974, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0625 = fieldNorm(doc=6974)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Source
- Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
-
Lam, W.; Chan, K.; Radev, D.; Saggion, H.; Teufel, S.: Context-based generic cross-lingual retrieval of documents and automated summaries (2005)
0.00
0.0035755006 = product of:
0.014302002 = sum of:
0.014302002 = product of:
0.042906005 = sum of:
0.042906005 = weight(_text_:language in 1965) [ClassicSimilarity], result of:
0.042906005 = score(doc=1965,freq=2.0), product of:
0.16497234 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.042049456 = queryNorm
0.26008 = fieldWeight in 1965, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.046875 = fieldNorm(doc=1965)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Abstract
- We develop a context-based generic cross-lingual retrieval model that can deal with different language pairs. Our model considers contexts in the query translation process. Contexts in the query as weIl as in the documents based an co-occurrence statistics from different granularity of passages are exploited. We also investigate cross-lingual retrieval of automatic generic summaries. We have implemented our model for two different cross-lingual settings, namely, retrieving Chinese documents from English queries as weIl as retrieving English documents from Chinese queries. Extensive experiments have been conducted an a large-scale parallel corpus enabling studies an retrieval performance for two different cross-lingual settings of full-length documents as weIl as automated summaries.
-
Uyttendaele, C.; Moens, M.-F.; Dumortier, J.: SALOMON: automatic abstracting of legal cases for effective access to court decisions (1998)
0.00
0.0033534872 = product of:
0.013413949 = sum of:
0.013413949 = product of:
0.040241845 = sum of:
0.040241845 = weight(_text_:29 in 495) [ClassicSimilarity], result of:
0.040241845 = score(doc=495,freq=2.0), product of:
0.14791684 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.042049456 = queryNorm
0.27205724 = fieldWeight in 495, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.0546875 = fieldNorm(doc=495)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Date
- 17. 7.1996 14:16:29
-
Ling, X.; Jiang, J.; He, X.; Mei, Q.; Zhai, C.; Schatz, B.: Generating gene summaries from biomedical literature : a study of semi-structured summarization (2007)
0.00
0.002979584 = product of:
0.011918336 = sum of:
0.011918336 = product of:
0.03575501 = sum of:
0.03575501 = weight(_text_:language in 946) [ClassicSimilarity], result of:
0.03575501 = score(doc=946,freq=2.0), product of:
0.16497234 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.042049456 = queryNorm
0.21673335 = fieldWeight in 946, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.0390625 = fieldNorm(doc=946)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Abstract
- Most knowledge accumulated through scientific discoveries in genomics and related biomedical disciplines is buried in the vast amount of biomedical literature. Since understanding gene regulations is fundamental to biomedical research, summarizing all the existing knowledge about a gene based on literature is highly desirable to help biologists digest the literature. In this paper, we present a study of methods for automatically generating gene summaries from biomedical literature. Unlike most existing work on automatic text summarization, in which the generated summary is often a list of extracted sentences, we propose to generate a semi-structured summary which consists of sentences covering specific semantic aspects of a gene. Such a semi-structured summary is more appropriate for describing genes and poses special challenges for automatic text summarization. We propose a two-stage approach to generate such a summary for a given gene - first retrieving articles about a gene and then extracting sentences for each specified semantic aspect. We address the issue of gene name variation in the first stage and propose several different methods for sentence extraction in the second stage. We evaluate the proposed methods using a test set with 20 genes. Experiment results show that the proposed methods can generate useful semi-structured gene summaries automatically from biomedical literature, and our proposed methods outperform general purpose summarization methods. Among all the proposed methods for sentence extraction, a probabilistic language modeling approach that models gene context performs the best.
-
Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007)
0.00
0.0028485605 = product of:
0.011394242 = sum of:
0.011394242 = product of:
0.034182724 = sum of:
0.034182724 = weight(_text_:22 in 948) [ClassicSimilarity], result of:
0.034182724 = score(doc=948,freq=2.0), product of:
0.14725003 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.042049456 = queryNorm
0.23214069 = fieldWeight in 948, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=948)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Abstract
- In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.
-
Sweeney, S.; Crestani, F.; Losada, D.E.: 'Show me more' : incremental length summarisation using novelty detection (2008)
0.00
0.0023953482 = product of:
0.009581393 = sum of:
0.009581393 = product of:
0.028744178 = sum of:
0.028744178 = weight(_text_:29 in 2054) [ClassicSimilarity], result of:
0.028744178 = score(doc=2054,freq=2.0), product of:
0.14791684 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.042049456 = queryNorm
0.19432661 = fieldWeight in 2054, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.0390625 = fieldNorm(doc=2054)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Date
- 29. 7.2008 19:35:12
-
Wang, S.; Koopman, R.: Embed first, then predict (2019)
0.00
0.0023953482 = product of:
0.009581393 = sum of:
0.009581393 = product of:
0.028744178 = sum of:
0.028744178 = weight(_text_:29 in 5400) [ClassicSimilarity], result of:
0.028744178 = score(doc=5400,freq=2.0), product of:
0.14791684 = queryWeight, product of:
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.042049456 = queryNorm
0.19432661 = fieldWeight in 5400, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5176873 = idf(docFreq=3565, maxDocs=44218)
0.0390625 = fieldNorm(doc=5400)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Date
- 29. 9.2019 12:18:42
-
Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006)
0.00
0.0023738004 = product of:
0.0094952015 = sum of:
0.0094952015 = product of:
0.028485604 = sum of:
0.028485604 = weight(_text_:22 in 5290) [ClassicSimilarity], result of:
0.028485604 = score(doc=5290,freq=2.0), product of:
0.14725003 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.042049456 = queryNorm
0.19345059 = fieldWeight in 5290, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=5290)
0.33333334 = coord(1/3)
0.25 = coord(1/4)
- Date
- 22. 7.2006 17:25:48
