Using term clouds to represent segment-level semantic content of podcasts

Spoken audio, like any time-continuous medium, is notoriously difficult to browse or skim without support of an interface providing semantically annotated jump points to signal the user where to listen in. Creation of time-aligned metadata by human annotators is prohibitively expensive, motivating the investigation of representations of segment-level semantic content based on transcripts generated by automatic speech recognition (ASR). This paper examines the feasibility of using term clouds to provide users with a structured representation of the semantic content of podcast episodes. Podcast episodes are visualized as a series of sub-episode segments, each represented by a term cloud derived from a transcript generated by automatic speech recognition (ASR). Quality of segment-level term clouds is measured quantitatively and their utility is investigated using a small-scale user study based on human labeled segment boundaries. Since the segment-level clouds generated from ASR-transcripts prove useful, we examine an adaptation of text tiling techniques to speech in order to be able to generate segments as part of a completely automated indexing and structuring system for browsing of spoken audio. Results demonstrate that the segments generated are comparable with human selected segment boundaries

Segmenting broadcast news streams using lexical chains

In this paper we propose a course-grained NLP approach to text segmentation based on the analysis of lexical cohesion within text. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e. distinct news stories from broadcast news programmes. Our system SeLeCT first builds a set of lexical chains, in order to model the discourse structure of the text. A boundary detector is then used to search for breaking points in this structure indicated by patterns of cohesive strength and weakness within the text. We evaluate this technique on a test set of concatenated CNN news story transcripts and compare it with an established statistical approach to segmentation called TextTiling

Segmentation of lecture videos based on text: A method combining multiple linguistic features

In multimedia-based e-Learning systems, there are strong needs for segmenting lecture videos into topic units in order to organize the videos for browsing and to provide search capability. Automatic segmentation is highly desired because of the high cost of manual segmentation. While a lot of research has been conducted on topic segmentation of transcribed spoken text, most attempts rely on domain-specific cues and formal presentation format, and require extensive training; none of these features exist in lecture videos with unscripted and spontaneous speech. In addition, lecture videos usually have few scene changes, which implies that the visual information that most video segmentation methods rely on is not available. Furthermore, even when there are scene changes, they do not match with the topic transitions. In this paper, we make use of the transcribed speech text extracted from the audio track of video to segment lecture videos into topics. We review related research and propose a new segmentation approach. Our approach utilizes features such as noun phrases and combines multiple content-based and discourse-based features. Our preliminary results show that the noun phrases are salient features and the combination of multiple features is promising to improve segmentation accuracy.published_or_final_versio

Exploring the subtopic-based relationship map strategy for multi-document summarization

In this paper we adapt and explore strategies for generating multi-document summaries based on relationship maps, which represent texts as graphs (maps) of interrelated segments and apply different traversing techniques for producing the summaries. In particular, we focus on the Segmented Bushy Path, a sophisticated method which tries to represent in a summary the main subtopics from source texts while keeping its informativeness. In addition, we also investigate some well-known subtopic segmentation and clustering techniques in order to correctly select the most relevant information to compose the final summary. We show that this subtopic-based method outperforms other methods for multi-document summarization and that achieves state of the art results, competing with the most sophisticated deep summarization methods in the area

Finding related sentence pairs in MEDLINE

We explore the feasibility of automatically identifying sentences in different MEDLINE abstracts that are related in meaning. We compared traditional vector space models with machine learning methods for detecting relatedness, and found that machine learning was superior. The Huber method, a variant of Support Vector Machines which minimizes the modified Huber loss function, achieves 73% precision when the score cutoff is set high enough to identify about one related sentence per abstract on average. We illustrate how an abstract viewed in PubMed might be modified to present the related sentences found in other abstracts by this automatic procedure

Using the organizational and narrative thread structures in an e-book to support comprehension.

Stories, themes, concepts and references are organized structurally and purposefully in most books. A person reading a book needs to understand themes and concepts within the context. Schanks Dynamic Memory theory suggested that building on existing memory structures is essential to cognition and learning. Pirolli and Card emphasized the need to provide people with an independent and improved ability to access and understand information in their information seeking activities. Through a review of users reading behaviours and of existing e-Book user interfaces, we found that current e-Book browsers provide minimal support for comprehending the content of large and complex books. Readers of an e-Book need user interfaces that present and relate the organizational and narrative structures, and moreover, reveal the thematic structures. This thesis addresses the problem of providing readers with effective scaffolding of multiple structures of an e-Book in the user interface to support reading for comprehension. Recognising a story or topic as the basic unit in a book, we developed novel story segmentation techniques for discovering narrative segments, and adapted story linking techniques for linking narrative threads in semi-structured linear texts of an e-Book. We then designed an e-Book user interface to present the complex structures of the e-Book, as well as to assist the reader to discover these structures. We designed and developed evaluation methodologies to investigate reading and comprehension in e-Books, in order to assess the effectiveness of this user interface. We designed semi-directed reading tasks using a Story-Theme Map, and a set of corresponding measurements for the answers. We conducted user evaluations with book readers. Participants were asked to read stories, to browse and link related stories, and to identify major themes of stories in an e-Book. This thesis reports the experimental design and results in detail. The results confirmed that the e-Book interface helped readers perform reading tasks more effectively. The most important and interesting finding is that the interface proved to be more helpful to novice readers who had little background knowledge of the book. In addition, each component that supported the user interface was evaluated separately in a laboratory setting and, these results too are reported in the thesis

MultiWiki: interlingual text passage alignment in Wikipedia

In this article we address the problem of text passage alignment across interlingual article pairs in Wikipedia. We develop methods that enable the identification and interlinking of text passages written in different languages and containing overlapping information. Interlingual text passage alignment can enable Wikipedia editors and readers to better understand language-specific context of entities, provide valuable insights in cultural differences and build a basis for qualitative analysis of the articles. An important challenge inthis context is the trade-off between the granularity of the extracted text passages and the precision of the alignment. Whereas short text passages can result in more precise alignment, longer text passages can facilitate a better overview of the differences in an article pair. To better understand these aspects from the user perspective, we conduct a user study at the example of the German, Russian and the English Wikipedia and collect a user-annotated benchmark. Then we propose MultiWiki – a method that adopts an integrated approach to the text passage alignment using semantic similarity measures and greedy algorithms and achieves precise results with respect to the user-defined alignment. MultiWiki demonstration is publicly available and currently supports four language pairs