Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

Explicit diversification of event aspects for temporal summarization

During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness

Multi-document Summarization Based on Sentence Clustering Improved Using Topic Words

Informasi dalam bentuk teks berita telah menjadi salah satu komoditas yang paling penting dalam era informasi ini. Ada banyak berita yang dihasilkan sehari-hari, tetapi berita-berita ini sering memberikan konten kontekstual yang sama dengan narasi berbeda. Oleh karena itu, diperlukan metode untuk mengumpulkan informasi ini ke dalam ringkasan sederhana. Di antara sejumlah subtugas yang terlibat dalam peringkasan multi-dokumen termasuk ekstraksi kalimat, deteksi topik, ekstraksi kalimat representatif, dan kalimat rep-resentatif. Dalam tulisan ini, kami mengusulkan metode baru untuk merepresentasikan kalimat ber-dasarkan kata kunci dari topic teks menggunakan Latent Dirichlet Allocation (LDA). Metode ini terdiri dari tiga langkah dasar. Pertama, kami mengelompokkan kalimat di set dokumen menggunakan kesamaan histogram pengelompokan (SHC). Selanjutnya, peringkat cluster menggunakan klaster penting. Terakhir, kalimat perwakilan yang dipilih oleh topik diidentifikasi pada LDA. Metode yang diusulkan diuji pada dataset DUC2004. Hasil penelitian menunjukkan rata-rata 0,3419 dan 0,0766 untuk ROUGE-1 dan ROUGE-2, masing-masing. Selain itu, dari pembaca prespective, metode kami diusulkan menyajikan pengaturan yang koheren dan baik dalam memesan kalimat representatif, sehingga dapat mempermudah pemahaman bacaan dan mengurangi waktu yang dibutuhkan untuk membaca ringkasan

Using the Annotated Bibliography as a Resource for Indicative Summarization

We report on a language resource consisting of 2000 annotated bibliography entries, which is being analyzed as part of our research on indicative document summarization. We show how annotated bibliographies cover certain aspects of summarization that have not been well-covered by other summary corpora, and motivate why they constitute an important form to study for information retrieval. We detail our methodology for collecting the corpus, and overview our document feature markup that we introduced to facilitate summary analysis. We present the characteristics of the corpus, methods of collection, and show its use in finding the distribution of types of information included in indicative summaries and their relative ordering within the summaries.Comment: 8 pages, 3 figure