[PDF] Top 20 Supervised Ranking in Open Domain Text Summarization

... The kappa agreement among subjects was 0.25. The result is in a way consistent with Salton et al. (1999), who report a low inter-subject agreement on paragraph extracts from encyclope- dias and also with Gong and Liu ... See full document

... based ranking algorithm for text ...based text ranking, with improved results comparative to ex- isting ranking ...the ranking process to be done in a time efficient ...vised ... See full document

... of open domain ...answer open- domain questions with a large-scale unlabeled cor- ...vised open-domain question answering (DS-QA) system which uses information retrieval ... See full document

... using supervised learning to measure the mean performance of different al- gorithms on a domain-specific training set and us- ing this knowledge to assign weights to the summaries derived by these ...same ... See full document

... Several supervised learning techniques which are the part of the Data Mining ...final ranking sentence function is ...a ranking composed of three sentences is also used to developed summary of the ... See full document

... Figure 1: An example of OpenQA. The key infor- mation, answers in correct-labeled and wrong-labeled contexts are marked in blue, green and red respectively. be seen in Figure 1, some of the negative para- graphs are ... See full document

... needed in our framework. In (Huang and Yates, 2010), the authors trained a HMM over the Brown test set and the WSJ unlabeled data. They derived features from Viterbi optimal states of single words and spans of words and ... See full document

... unstructured text cor- pus (Brill et ...of text data available on the ...corpus-based open-domain QA (Chen et ...re- ranking the documents based on the likelihood of containing answer ... See full document

... Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user and ...Automatic Text Summarization. ... See full document

... For a more intuitive understanding of the improve- ments from ORQA, we compare its predictions with baseline predictions in Table 7. We find that ORQA is more robust at separating semantically distinct text with ... See full document

... Automatic text summarization is one of the most growing fields of research in the field of natural language processing which reduces the content of text in such a manner that the main thought of the ... See full document

... For each topic, we only used the topic title as the topic description. As a pre-processing step, we removed the very long or very short sentences, which are usually not good summary sentences. We also polished some ... See full document

... with Text summarization which is the process of automatically creating a shorter version of one or more text ...Essentially, text summarization techniques are classified as Extractive ... See full document

... Multi-document Summarization accepts several documents at a time as ...document summarization technique for generating summaries usually do not depend upon the structure of the document because structure of ... See full document

... The automatic summarization procedure is shown in Figure 1. First, each input sentence is examined by going through the pre-specified fea- ture functions. The classification model will then predict the importance ... See full document

... a text coherent, sometimes the same words are used in two successive ...automatic text summarization research, this repetition is referred to as Lexical Chain and can be leveraged to find important ... See full document

... Cheng-BinJin, Shengzhe Li, and Hakil Kim [3] in his paper discusses about knowing the activity being done in the video with the help of three levels that is posture, locomotion and gesture level with multi CNN.The author ... See full document

... the Text Analysis Conference (TAC) (previously called the Document Understanding Conference (DUC)) (Nat, ...multi-document summarization: machine summa- rization of sets of related documents, sometimes ... See full document

... One way to improve the accuracy of auto- matic speech recognition (ASR) is to use dis- criminative language modeling (DLM), which enhances discrimination by learning where the ASR hypotheses deviate from the uttered ... See full document

... categories, supervised learning and unsupervised ...on Supervised Learning methods, we will instead differentiate our techniques through the inherent architectural differences of the ... See full document