Intra-Speaker Topic Modeling for Improved Multi-Party Meeting Summarization with Integrated Random Walk

2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 377–381, Montr´eal, Canada, June 3-8, 2012. c⃝2012 Association for Computational Linguistics Intra-Speaker Topic Modeling for Improved Multi-Party Meeting Summarization with Integrated Random Walk Yun-Nung Chen and Florian Metze School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213-3891, USA {yvchen,fmetze}@cs.cmu.edu Abstract This paper proposes an improved approach to extrac- tive summarization of spoken multi-party interac- tion, in which integrated random walk is performed on a graph constructed on topical/ lexical relations. Each utterance is represented as a node of the graph, and the edges’ weights are computed from the topi- cal similarity between the utterances, evaluated us- ing probabilistic latent semantic analysis (PLSA), and from word overlap. We model intra-speaker topics by partially sharing the topics from the same speaker in the graph. In this paper, we perform ex- periments on automatically and manually generated transcripts. For automatic transcripts, our results show that intra-speaker topic sharing and integrating topical/ lexical relations can help include the impor- tant utterances. 1 Introduction Speech summarization is an active and important topic of research (Lee and Chen, 2005), because multimedia/ spo- ken documents are more difficult to browse than text or image content. While earlier work was focused primarily on broadcast news content, recent effort has been increas- ingly directed to new domains such as lectures (Glass et al., 2007; Chen et al., 2011) and multi-party interac- tion (Banerjee and Rudnicky, 2008; Liu and Liu, 2010). We describe experiments on multi-party interaction found in meeting recordings, performing extractive summariza- tion (Liu et al., 2010) on transcripts generated by auto- matic speech recognition (ASR) and human annotators. Graph-based methods for computing lexical centrality as importance to extract summaries (Erkan and Radev, 2004) have been investigated in the context of text sum- marization. Some works focus on maximizing cover- age of summaries using the objective function (Gillick, 2011). Speech summarization carries intrinsic difficul- ties due to the presence of recognition errors, sponta- neous speech effect, and lack of segmentation. A gen- eral approach has been found very successful (Furui et al., 2004), in which each utterance in the document d, U = t1t2...ti...tn, represented as a sequence of terms ti, is given an importance score: I(U, d) = 1 n n � i=1 [λ1s(ti, d) + λ2l(ti) (1) + λ3c(ti) + λ4g(ti)] + λ5b(U), where s(ti, d), l(ti), c(ti), g(ti) are respectively some statistical measure (such as TF-IDF), linguistic measure (e.g., different part-of-speech tags are given different weights), confidence score and N-gram score for the term ti, and b(U) is calculated from the grammatical structure of the utterance U, and λ1, λ2, λ3, λ4 and λ5 are weight- ing parameters. For each document, the utterances to be used in the summary are then selected based on this score. In recent work, Chen (2011) proposed a graphical structure to rescore I(U, d), which can model the topical coherence between utterances using random walk within documents. Similarly, we …

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