Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document Summarization

• URL: http://arxiv.org/abs/2108.01441v1
• Date: Sat, 31 Jul 2021 02:20:44 GMT
• Title: Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document Summarization
• Authors: Quanye Jia, Rui Liu and Jianying Lin
• Abstract summary: We present a query expansion method, which is combined in the manifold ranking to resolve this problem. Our method not only utilizes the information of the query term itself and the knowledge base WordNet to expand it by synonyms, but also uses the information of the document set itself to expand the query in various ways. In addition, we use the degree of word overlap and the proximity between words to calculate the similarity between sentences.
• Score: 3.146785346730256
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Manifold ranking has been successfully applied in query-oriented multi-document summarization. It not only makes use of the relationships among the sentences, but also the relationships between the given query and the sentences. However, the information of original query is often insufficient. So we present a query expansion method, which is combined in the manifold ranking to resolve this problem. Our method not only utilizes the information of the query term itself and the knowledge base WordNet to expand it by synonyms, but also uses the information of the document set itself to expand the query in various ways (mean expansion, variance expansion and TextRank expansion). Compared with the previous query expansion methods, our method combines multiple query expansion methods to better represent query information, and at the same time, it makes a useful attempt on manifold ranking. In addition, we use the degree of word overlap and the proximity between words to calculate the similarity between sentences. We performed experiments on the datasets of DUC 2006 and DUC2007, and the evaluation results show that the proposed query expansion method can significantly improve the system performance and make our system comparable to the state-of-the-art systems.

Related papers

• Optimization of Retrieval-Augmented Generation Context with Outlier Detection [0.0]
  We focus on methods to reduce the size and improve the quality of the prompt context required for question-answering systems. Our goal is to select the most semantically relevant documents, treating the discarded ones as outliers. It was found that the greatest improvements were achieved with increasing complexity of the questions and answers.
  arXiv  Detail & Related papers  (2024-07-01T15:53:29Z)
• Database-Augmented Query Representation for Information Retrieval [59.57065228857247]
  We present a novel retrieval framework called Database-Augmented Query representation (DAQu) DAQu augments the original query with various (query-related) metadata across multiple tables. We validate DAQu in diverse retrieval scenarios that can incorporate metadata from the relational database.
  arXiv  Detail & Related papers  (2024-06-23T05:02:21Z)
• User Intent Recognition and Semantic Cache Optimization-Based Query Processing Framework using CFLIS and MGR-LAU [0.0]
  This work analyzed the informational, navigational, and transactional-based intents in queries for enhanced QP. For efficient QP, the data is structured using Epanechnikov Kernel-Ordering Points To Identify the Clustering Structure (EK-OPTICS) The extracted features, detected intents and structured data are inputted to the Multi-head Gated Recurrent Learnable Attention Unit (MGR-LAU)
  arXiv  Detail & Related papers  (2024-06-06T20:28:05Z)
• CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion [68.19934563919192]
  We propose a curriculum sampling strategy that utilizes pseudo queries during training and progressively enhances the relevance between the generated query and the real query. Experimental results on both in-domain and out-of-domain datasets demonstrate that our approach outperforms previous dense retrieval models.
  arXiv  Detail & Related papers  (2022-12-18T15:57:46Z)
• Query Expansion Using Contextual Clue Sampling with Language Models [69.51976926838232]
  We propose a combination of an effective filtering strategy and fusion of the retrieved documents based on the generation probability of each context. Our lexical matching based approach achieves a similar top-5/top-20 retrieval accuracy and higher top-100 accuracy compared with the well-established dense retrieval model DPR. For end-to-end QA, the reader model also benefits from our method and achieves the highest Exact-Match score against several competitive baselines.
  arXiv  Detail & Related papers  (2022-10-13T15:18:04Z)
• Learning Query Expansion over the Nearest Neighbor Graph [94.80212602202518]
  Graph Query Expansion (GQE) is presented, which is learned in a supervised manner and performs aggregation over an extended neighborhood of the query. The technique achieves state-of-the-art results over known benchmarks.
  arXiv  Detail & Related papers  (2021-12-05T19:48:42Z)
• Text Summarization with Latent Queries [60.468323530248945]
  We introduce LaQSum, the first unified text summarization system that learns Latent Queries from documents for abstractive summarization with any existing query forms. Under a deep generative framework, our system jointly optimize a latent query model and a conditional language model, allowing users to plug-and-play queries of any type at test time. Our system robustly outperforms strong comparison systems across summarization benchmarks with different query types, document settings, and target domains.
  arXiv  Detail & Related papers  (2021-05-31T21:14:58Z)
• Attention-Based Query Expansion Learning [14.992170588260173]
  An important aspect of query expansion is choosing an appropriate way to combine the images into a new query. In this paper we propose a more principled framework to query expansion, where one trains, in a discriminative manner, a model that learns how images should be aggregated to form the expanded query.
  arXiv  Detail & Related papers  (2020-07-15T22:15:55Z)
• Query Focused Multi-Document Summarization with Distant Supervision [88.39032981994535]
  Existing work relies heavily on retrieval-style methods for estimating the relevance between queries and text segments. We propose a coarse-to-fine modeling framework which introduces separate modules for estimating whether segments are relevant to the query. We demonstrate that our framework outperforms strong comparison systems on standard QFS benchmarks.
  arXiv  Detail & Related papers  (2020-04-06T22:35:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.