Model-based Story Summary

A story summarizer benefits greatly from a reader model because a reader model enables the story summarizer to focus on delivering useful knowledge in minimal time with minimal effort. Such a summarizer can, in particular, eliminate disconnected story elements, deliver only story elements connected to conceptual content, focus on particular concepts of interest, such as revenge, and make use of our human tendency to see causal connection in adjacent sentences. Experiments with a summarizer, built on the Genesis story understanding system, demonstrate considerable compression of an 85-element precis of the plot of Shakespeare’s Macbeth, reducing it, for example, to the 14 elements that make it a concise summary about Pyrrhic victory. Refocusing the summarizer on regicide reduces the element count to 7, or 8% of the original

TVStoryGen: A Dataset for Generating Stories with Character Descriptions

We introduce TVStoryGen, a story generation dataset that requires generating detailed TV show episode recaps from a brief summary and a set of documents describing the characters involved. Unlike other story generation datasets, TVStoryGen contains stories that are authored by professional screen-writers and that feature complex interactions among multiple characters. Generating stories in TVStoryGen requires drawing relevant information from the lengthy provided documents about characters based on the brief summary. In addition, we propose to train reverse models on our dataset for evaluating the faithfulness of generated stories. We create TVStoryGen from fan-contributed websites, which allows us to collect 26k episode recaps with 1868.7 tokens on average. Empirically, we take a hierarchical story generation approach and find that the neural model that uses oracle content selectors for character descriptions demonstrates the best performance on automatic metrics, showing the potential of our dataset to inspire future research on story generation with constraints. Qualitative analysis shows that the best-performing model sometimes generates content that is unfaithful to the short summaries, suggesting promising directions for future work

Keefektifan Strategi Writing a Story Based on a Picture/Photograph dan Summary Writing dalam Pembelajaran Menulis Teks Eksplanasi Kompleks pada Siswa Kelas XI SMAN 10 Yogyakarta

Penelitian ini bertujuan untuk menguji (1) keefektifan strategi writing a story based on a picture/photograph (WSBP) dibandingkan strategi summary writing (SW); (2) keefektifan strategi writing a story based on a picture/photograph dibandingkan model konvensional; dan (3) keefektifan strategi summary writing dibandingkan model konvensional. Metode yang digunakan dalam penelitian ini adalah true experiment tipe pretest and post-test random assignment control group design. Populasi yang diambil adalah 131 siswa kelas XI MIPA di SMAN 10 Yogyakarta. Penentuan sampel menggunakan teknik cluster random sampling. Berdasarkan penentuan tersebut siswa kelas XI MIPA 3 sebagai kelompok eksperimen 1, XI MIPA 1 sebagai kelompok eksperimen 2, dan XI MIPA 2 sebagai kelompok kontrol. Pengumpulan data menggunakan teknik nontes berupa penugasan menulis teks eksplanasi kompleks dan pengamatan. Validitas yang digunakan dalam penelitian ini adalah validitas isi yang dikonsultasikan kepada ahli dengan hasil sudah siap dipergunakan untuk penelitian. Reliabilitas instrumen dianalisis menggunakan rumus Alpha Cronbach dan hasil penghitungan menunjukkan nilai 0.706. Teknik analisis data yang digunakan adalah analisis varians satu jalan. Hasil penelitian menunjukkan bahwa (1) Strategi WSBP dan strategi SW mampu mengembangkan ide atau gagasan siswa dalam menulis teks eksplanasi kompleks. Hasil tes akhir atau posttest menunjukkan bahwa nilai pretest dan posttest pada kelompok eksperimen 1 mengalami kenaikan sebesar 16,66%, pada kelompok eksperimen 2 mengalami kenaikan sebesar 6,52% dan pada kelompok kontrol mengalami kenaikan sebesar 7,86%. (2) Strategi writing a story based on a picture/photograph sama-sama efektif dengan strategi summary writing. Strategi writing a story based on a picture/photograph sama-sama efektif dengan model konvensional pada saat digunakan dalam pembelajaran menulis teks eksplanasi kompleks. (3) Strategi summary writing sama-sama efektif dengan model konvensional pada saat digunakan dalam pembelajaran menulis teks eksplanasi kompleks. Dengan demikian, ketiga strategi yang digunakan sama-sama efektif dalam pembelajaran menulis teks eksplanasi kompleks. Kata kunci: writing a story based on a picture/photograph, keefektifan, menulis, strategi, teks, eksplanasi kompleks, summary writin

Hierarchically-Attentive RNN for Album Summarization and Storytelling

We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines.Comment: To appear at EMNLP-2017 (7 pages

Using Approximate Bayesian Computation by Subset Simulation for Efficient Posterior Assessment of Dynamic State-Space Model Classes

Approximate Bayesian Computation (ABC) methods have gained in popularity over the last decade because they expand the horizon of Bayesian parameter inference methods to the range of models for which an analytical formula for the likelihood function might be difficult, or even impossible, to establish. The majority of the ABC methods rely on the choice of a set of summary statistics to reduce the dimension of the data. However, as has been noted in the ABC literature, the lack of convergence guarantees induced by the absence of a vector of sufficient summary statistics that assures intermodel sufficiency over the set of competing models hinders the use of the usual ABC methods when applied to Bayesian model selection or assessment. In this paper, we present a novel ABC model selection procedure for dynamical systems based on a recently introduced multilevel Markov chain Monte Carlo method, self-regulating ABC-SubSim, and a hierarchical state-space formulation of dynamic models. We show that this formulation makes it possible to independently approximate the model evidence required for assessing the posterior probability of each of the competing models. We also show that ABC-SubSim not only provides an estimate of the model evidence as a simple by-product but also gives the posterior probability of each model as a function of the tolerance level, which allows the ABC model choices made in previous studies to be understood. We illustrate the performance of the proposed framework for ABC model updating and model class selection by applying it to two problems in Bayesian system identification: a single-degree-of-freedom bilinear hysteretic oscillator and a three-story shear building with Masing hysteresis, both of which are subject to a seismic excitation