Multi-Document Summarization via Discriminative Summary Reranking

Existing multi-document summarization systems usually rely on a specific summarization model (i.e., a summarization method with a specific parameter setting) to extract summaries for different document sets with different topics. However, according to our quantitative analysis, none of the existing summarization models can always produce high-quality summaries for different document sets, and even a summarization model with good overall performance may produce low-quality summaries for some document sets. On the contrary, a baseline summarization model may produce high-quality summaries for some document sets. Based on the above observations, we treat the summaries produced by different summarization models as candidate summaries, and then explore discriminative reranking techniques to identify high-quality summaries from the candidates for difference document sets. We propose to extract a set of candidate summaries for each document set based on an ILP framework, and then leverage Ranking SVM for summary reranking. Various useful features have been developed for the reranking process, including word-level features, sentence-level features and summary-level features. Evaluation results on the benchmark DUC datasets validate the efficacy and robustness of our proposed approach

Population analysis of neural data -- developments in statistical methods and related computational models

A key goal of neuroscience is to understand how the remarkable computational abilities of our brain emerge as a result of interconnected neuronal populations. Recently, advances in technologies for recording neural activity have increased the number of simultaneously recorded neurons by orders of magnitude, and these technologies are becoming more widely adopted. At the same time, massive increases in computational power and improved algorithms have enabled advanced statistical analyses of neural population activity and promoted our understanding of population coding. Nevertheless, there are many unanswered emerging questions, when it comes to analyzing and interpreting neural recordings. There are two major parts to this study. First, we consider an issue of increasing importance: that many in vivo recordings are now made by calcium-dependent fluorescent imaging, which only indirectly reports neural activity. We compare measurements of extracellular single units with fluorescence changes extracted from single neurons (often used as a proxy for spike rates), both recorded from cortical neural populations of behaving mice. We perform identical analyses at the single cell level and population level, and compare the results, uncovering a number of differences, or biases. We propose a phenomenological model to transform spike trains into synthetic imaging data and test whether the transformation explains the biases found. We discover that the slow temporal dynamics of calcium imaging obscure rapid changes in neuronal selectivity and disperse dynamic features in time. As a result, spike rate modulation that is locked to temporally localized events can appear as a more sequence-like pattern of activity in the imaging data. In addition, calcium imaging is more sensitive to increases rather than decreases in spike rate, leading to biased estimates of neural selectivity. These biases need to be considered when interpreting calcium imaging data. The second part of this work embarks on a challenging yet fruitful study of latent variable analysis of simultaneously recorded neural activity in a decision-making task. To connect the neural dynamics in different stages of a decision-making task, we developed a time-varying latent dynamics system model that uncovers neural dynamics shared by neurons in a local decision-making circuit. The shared neural activity supports the dynamics of choice generation and memory in a fashion akin to drift diffusion models, and robustly maintains a decision signal in the post-decision period. Importantly, we find that error trials follow similar dynamics to those of correct trials, but their dynamics are separated in shared neural activity space, proving a more correct early decoding estimation of an animal's success or failure at a given trial. Overall, the shared neural activity dynamics can predict multiple measures of behavioral variability including performance, reaction time, and trial correctness, and therefore are a useful summary of the neural representation. Such an approach can be readily applied to study complex dynamics in other neural systems. In summary, this dissertation represents an important step towards developing model-based analysis of neuronal dynamics and understanding population codes in large-scale neural data

TGSum: Build Tweet Guided Multi-Document Summarization Dataset

The development of summarization research has been significantly hampered by the costly acquisition of reference summaries. This paper proposes an effective way to automatically collect large scales of news-related multi-document summaries with reference to social media's reactions. We utilize two types of social labels in tweets, i.e., hashtags and hyper-links. Hashtags are used to cluster documents into different topic sets. Also, a tweet with a hyper-link often highlights certain key points of the corresponding document. We synthesize a linked document cluster to form a reference summary which can cover most key points. To this aim, we adopt the ROUGE metrics to measure the coverage ratio, and develop an Integer Linear Programming solution to discover the sentence set reaching the upper bound of ROUGE. Since we allow summary sentences to be selected from both documents and high-quality tweets, the generated reference summaries could be abstractive. Both informativeness and readability of the collected summaries are verified by manual judgment. In addition, we train a Support Vector Regression summarizer on DUC generic multi-document summarization benchmarks. With the collected data as extra training resource, the performance of the summarizer improves a lot on all the test sets. We release this dataset for further research.Comment: 7 pages, 1 figure in AAAI 201

Loop-current charge density wave driven by long-range Coulomb repulsion on the kagome lattice

Recent experiments on vanadium-based nonmagnetic kagom\'e metals $A$V$_3$Sb$_5$ ($A=$ K, Rb, Cs) revealed evidence for possible spontaneous time-reversal symmetry (TRS) breaking in the charge density wave (CDW) ordered state. The long-sought-after quantum order of loop currents has been suggested as a candidate for the TRS breaking state. However, a microscopic model for the emergence of the loop-current CDW due to electronic correlations is still lacking. Here, we calculate the susceptibility of the real and imaginary bond orders on the kagom\'e lattice near van Hove filling, and reveal the importance of next-nearest-neighbor Coulomb repulsion $V_2$ in triggering the instability toward imaginary bond ordered CDW. The concrete effective single-orbital $t$-$V_1$-$V_2$ model on the kagom\'e lattice is then studied, where $t$ and $V_1$ are the hopping and Coulomb repulsion on the nearest-neighbor bonds. We obtain the mean-field ground states, analyze their properties, and determine the phase diagram in the plane spanned by $V_1$ and $V_2$ at van Hove filling. The region dominated by $V_1$ is occupied by a $2a_0 \times 2a_0$ real CDW insulator with the inverse of Star-of-David (ISD) bond configuration. Increasing $V_2$ indeed drives a first-order transition from the ISD to stabilized loop-current insulators that exhibit four possible current patterns of different topological properties, leading to orbital Chern insulators. We then extend these results away from van Hove filling and show that the loop-current states survive in the lightly doped orbital Chern insulators, and give rise to emergent Chern Fermi pockets carrying large Berry curvature and orbit magnetic moment. Our findings provide a concrete model realization of the loop-current Chern metal at the mean-field level for the TRS breaking normal state of the kagom\'e superconductors.Comment: 11 pages, 6 figure

Intertwined charge and pair density orders in a monolayer high-Tc iron-based superconductor

Symmetry-breaking electronic phase in unconventional high-temperature (high-Tc) superconductors is a fascinating issue in condensed-matter physics, among which the most attractive phases are charge density wave (CDW) phase with four unit-cell periodicity in cuprates and nematic phase breaking the C4 rotational symmetry in iron-based superconductors (FeSCs). Recently, pair density wave (PDW), an exotic superconducting phase with non-zero momentum Cooper pairs, has been observed in high-Tc cuprates and the monolayer FeSC. However, the interplay between the CDW, PDW and nematic phase remains to be explored. Here, using scanning tunneling microscopy/spectroscopy, we detected commensurate CDW and CDW-induced PDW orders with the same period of lambda = 4aFe (aFe is the distance between neighboring Fe atoms) in a monolayer high-Tc Fe(Te,Se) film grown on SrTiO3(001) substrate. Further analyses demonstrate the observed CDW is a smectic order, which breaks both translation and C4 rotational symmetry. Moreover, the smecticity of the CDW order is strongest near the superconducting gap but weakens near defects and in an applied magnetic field, indicating the interplay between the smectic CDW and PDW orders. Our works provide a new platform to study the intertwined orders and their interactions in high-Tc superconductors