The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper proposes a novel sentence ranking approach to query-biased summarization where ranking performance can be boosted by encouraging biased information richness in a multi-view framework. To investigate how the final ranking result can benefit from diverse local ranking's combination, the proposed approach firstly constructs two base rankers to rank all the sentences in a document set from...
There is an important issue that text summarization has to embody personal information need and provide indicative message to user. In this paper, a method of acquiring relevant documents based on user-feedback information and transductive inference SVM machine learning is presented. This method can well avoid the subjectivity of deciding relevant documents empirically. Furthermore, a sentence selection...
Genomic Information Retrieval which contains huge highly specific information causes many problems, such as the synonym problem, long term name and rapid growing literature size. In this paper, we use a concept-based model for indexing and querying, which is not like the translation model or the traditional query expansion techniques. We adopt an extraction tool, MaxMatcher, which using Universal...
In this paper we propose a novel method to estimate the relevance between query and candidate expansion terms for Chinese information retrieval. In previous method, expansion terms are usually selected by counting term co-occurrences in the documents. However, term co-occurrences are not always a good indicator for relevance, whereas some are background terms of the whole collection. In order to remove...
This paper proposes a strategy of the summary sentence selection for query-focused multi-document summarization through extracting keywords from relevant document set. It calculates the query related feature and the topic related feature for every word in relevant document set, then obtains the importance of the word by combining the two features. The score of candidate sentence is computed through...
There is an important issue that text summarization has to embody the personal information need and provide the indicative message for user. In this paper, a method of acquiring relevant documents based on user-feedback information and transductive inference SVM machine learning technology is presented. This method can well avoid subjectivity of deciding relevant documents empirically. To validate...
In this paper we present a Chinese query expansion model based on topic-relevant terms which were acquired from the Google search engine automatically. In contrast to earlier methods, our queries are expanded by adding those terms that are most relevant to the concept of the query, rather than selecting terms that are relevant to the query terms. Firstly, we use automatically extracted short terms...
This paper presents how to use ROUGE to evaluate summaries without human reference summaries. ROUGE is a widely used evaluation tool for multi-document summarization and has great advantages in the areas of summarization evaluation. However, manual reference summaries written beforehand by assessors are indispensable for a ROUGE test. There was still no research on ROUGEpsilas abilities of evaluating...
The most important step of query-focused extractive summarization is deciding which sentences are appropriately included in the final summary. In this paper, we propose a feature fusion based sentence selecting strategy, to identify the sentences with high query-relevance and high information density. We score each sentence by computing its similarity and Skip-Bigram co-occurrence with query. These...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.