Two-layer mutually reinforced random walk for improved multi-party meeting summarization

Chen, Yun-Nung; Metze, Florian

doi:10.1109/slt.2012.6424268

Cited by 26 publications

(28 citation statements)

References 17 publications

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“…We first compare our model with three widely used clustering methods: k-means clustering, DBSCAN clustering [7] and aforementioned AP clustering [11]. Besides, we also adopt the mutually reinforced random walk model [4] (denoted as MRRW) to judge entity typicality based on the hypothesis that typical exemplars are those who are similar to the other members of its category and dissimilar to members of the contrast categories. Finally, we also test a limited version of our approach called independent ILP (denoted as I-ILP) that separately identifies exemplars of each input sets based on our proposed ILP framework.…”

Section: Baselinesmentioning

confidence: 99%

Mapping Entity Sets in News Archives Across Time

et al. 2019

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We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to automatically generate similar entity pairs given two sets of entities, one from the past and one representing the present. This allows performing entity-oriented mapping between different times. We introduce an effective method to solve the aforementioned task based on a concise integer linear programming framework. In particular, our model first conducts typicality analysis to estimate entity representativeness. It next constructs orthogonal transformation between the two entity collections. The result is a set of typical across-time comparables. We demonstrate the effectiveness of our approach on the New York Times dataset through both qualitative and quantitative tests.

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Section: Baselinesmentioning

confidence: 99%

Mapping Entity Sets in News Archives Across Time

et al. 2019

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“…A feature graph is constructed to represent the two documents. Graph‐based summarization approaches have been used for many years . In our approach, the nodes in the graph represent the features, and the edges represent the relationships between features.…”

Section: The Summarization Modulementioning

confidence: 99%

“…Graph-based summarization approaches have been used for many years. [35][36][37] In our approach, the nodes in the graph represent the features, and the edges represent the relationships between features. Common nodes represent the topic keywords that occur in both documents.…”

Section: Feature Graph Constructionmentioning

confidence: 99%

Automatic generation of related work through summarizing citations

Chen

Zhuge

2017

Concurrency and Computation

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Summary Related work is a component of a scientific paper, which introduces other researchers' relevant works and makes comparisons with the current author's work. Automatically generating the related work section of a writing paper provides a tool for researchers to accomplish the related work section efficiently without missing related works. This paper proposes an approach to automatically generating a related work section by comparing the main text of the paper being written with the citations of other papers that cite the same references. Our approach first collects the papers that cite the reference papers of the paper being written and extracts the corresponding citation sentences to form a citation document. It then extracts keywords from the citation document and the paper being written and constructs a graph of the keywords. Once the keywords that discriminate the two documents are determined, the minimum Steiner tree that covers the discriminative keywords and the topic keywords is generated. The summary is generated by extracting the sentences covering the Steiner tree. According to ROUGE evaluations, the experiments show that the citations are suitable for related work generation and our approach outperforms the three baseline methods of MEAD, LexRank, and ReWoS. This work verifies the general summarization method based on connotation and extension through citation.

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“…and E ws correspond to slot-to-slot relations, wordto-word relations, and word-to-slot relations respectively (Chen and Metze, 2012;Chen and Metze, 2013).…”

Section: Knowledge Graphsmentioning

confidence: 99%

Jointly Modeling Inter-Slot Relations by Random Walk on Knowledge Graphs for Unsupervised Spoken Language Understanding

Chen¹,

Wang²,

Rudnicky³

2015

Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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A key challenge of designing coherent semantic ontology for spoken language understanding is to consider inter-slot relations. In practice, however, it is difficult for domain experts and professional annotators to define a coherent slot set, while considering various lexical, syntactic, and semantic dependencies. In this paper, we exploit the typed syntactic dependency theory for unsupervised induction and filling of semantics slots in spoken dialogue systems. More specifically, we build two knowledge graphs: a slot-based semantic graph, and a word-based lexical graph. To jointly consider word-to-word, word-toslot, and slot-to-slot relations, we use a random walk inference algorithm to combine the two knowledge graphs, guided by dependency grammars. The experiments show that considering inter-slot relations is crucial for generating a more coherent and compete slot set, resulting in a better spoken language understanding model, while enhancing the interpretability of semantic slots.

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Two-layer mutually reinforced random walk for improved multi-party meeting summarization

Cited by 26 publications

References 17 publications

Mapping Entity Sets in News Archives Across Time

Mapping Entity Sets in News Archives Across Time

Automatic generation of related work through summarizing citations

Jointly Modeling Inter-Slot Relations by Random Walk on Knowledge Graphs for Unsupervised Spoken Language Understanding

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